THE PSYCHOPHYSIOLOGICAL
EVALUATION OF THE PLAYER
EXPERIENCE
Madison Klarkowski B. Games & Interactive Entertainment, Honours (IT).
Written under the supervision of Assoc. Prof. Daniel Johnson & Assoc. Prof. Peta Wyeth Assoc. Prof. Simon Smith
Submitted in fulfilment of the requirements for the degree of Doctor of Philosophy
School of Electrical Engineering and Computer Science Science and Engineering Faculty Queensland University of Technology 2017
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Keywords
Challenge; Challenge‒skill balance; Electrocardiography; Electrodermal activity;
Electroencephalography; Electromyography; Enjoyment; Flow; Physiology; Player experience;
Psychophysiology; Self-determination theory; Video games
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Abstract
As video games emerge as a leading form of entertainment, so too does the need for a comprehensive understanding of the player experience. Player experience research thus expands upon this understanding through the lens of psychological constructs such as flow, presence, challenge, competence and self-determination theory.
The psychophysiological method presents one approach for evaluating this player experience. A variety of psychophysiological investigations of the player experience have been undertaken, and have contributed novel results that expand upon the understanding of physiological response to video game play. However, these assessments often feature small sample sizes (occasionally only comprising participants of a single gender), or are restricted to employing one or two psychophysiological measures. The value of a study with a large sample size and multiple psychophysiological and subjective measures was thus identified, and undertaken for this program of research.
For this study, pilot testing was undertaken to confirm the suitability of the chosen psychophysiological measures, refine the game artefacts and identify the most appropriate subjective measures to use. The full study was conducted with 90 participants playing three game conditions that were manipulated in terms of challenge‒skill balance. These conditions featured one optimal player experience, ‘Balance’ (in which the challenges of the game matched the skills of the player’), and two sub-optimal player experiences, ‘Overload’ and ‘Boredom’ (in which the challenges of the game outstripped, or were outstripped by, the skills of the player). The full study featured the use of both subjective (Player Experience of Needs Satisfaction scale [PENS], flow and Intrinsic Motivation Inventory [IMI]) and psychophysiological (electrodermal activity [EDA],
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electromyography [EMG], electrocardiography [ECG] and electroencephalography [EEG]) measures.
Psychophysiological assessment revealed increased positively valenced emotional expressivity associated with increased challenge of the condition; greater EDA was found in the
Overload condition than in the Boredom condition; and decreased high-frequency (HF) peak components of heart rate variability (HRV) were found in the Overload condition compared with either Boredom or Balance. Results also revealed increased heart rate (HR) in the Boredom condition. Greater EEG alpha, beta and theta activity was also found in Balance and Overload conditions.
These results suggest increased positive valence with challenge, and greater presence of arousal in the Balance (optimal) and Overload conditions; however, increased HR in the Boredom condition indicates some complexities in interpreting psychophysiological data or assessing sub- optimal player experiences. Results for cognitive activity suggest greater alertness, creativity, attentional focus, problem-solving and restfulness in the Balance and Overload conditions, as assessed by electroencephalographic alpha, beta and theta frequency bands. Predictive relationships between physiological responses and specific subjective responses were not found, suggesting that psychophysiological evaluation may be limited in predicting individual components of the player experience.
Overall, this study identifies psychophysiological evaluation as an insightful and distinctive approach for assessing the player experience. It proposes recommendations for employing this approach alongside subjective analysis. Despite this, limitations exist for using psychophysiological evaluation in terms of its temporal, financial and methodological costs; however, these limitations may be minimised through certain approaches.
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List of Publications
Klarkowski, M., Johnson, D., Wyeth, P., McEwan, M., Phillips, C., & Smith, S. (2016). Operationalising and Evaluating Sub-Optimal and Optimal Play Experiences through Challenge‒Skill Manipulation. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems (CHI ’16), 5583‒5594, ACM, Santa Clara, CA. doi: 10.1145/2858036.2858563 Klarkowski, M., Johnson, D., Wyeth, P., Phillips, C., & Smith, S. (2016). Psychophysiology of Challenge in Play: EDA and Self-Reported Arousal. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA ’16), 1930‒1936, ACM, Santa Clara, CA. doi: 10.1145/2851581.2892485 Klarkowski, M., Johnson, D., Wyeth, P., Smith, S., & Phillips, C. (2015). Operationalising and Measuring Flow in Video Games. Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction (OzCHI ’15), 114 118, ACM, Melbourne, Australia. doi: 10.1145/2838739.2838826
Other publications include:
Vella, K., Cheng, V. W. S., Johnson, D., Mitchell, J., Davenport, T., Klarkowski, M., & Phillips, C. (2017). Pokémon GO and Social Connectedness. Manuscript submitted for publication. Vella, K., Klarkowski, M., Johnson, D., Hides, L., & Wyeth, P. (2016). The social context of video game play: Challenges and strategies. Proceedings of the Designing Interactive Systems Conference (DIS ’16), 761‒772, ACM, Brisbane, Australia. doi: 10.1145/2901790.2901823 Phillips, C., Johnson, D., Wyeth, P., Hides, L., & Klarkowski, M. (2015). Redefining Videogame Reward Types. Proceedings of the Annual Meeting of the Australian Special Interest Group for Human Interaction (OzCHI ’15), 83‒91, ACM, Melbourne, Australia. doi: 10.1145/2838739.2838782 Johnson, D., Wyeth, P., Clark, M., & Watling, C. (2015). Cooperative Game Play with Avatars and Agents: Differences in Brain Activity and the Experience of Play. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI ’15), 3721‒3730, ACM, Seoul, Republic of Korea. doi: 10.1145/2702123.2702468
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List of Figures
Figure 1. The flow channel ...... 11 Figure 2. Divisions of the nervous system...... 23 Figure 3. Russell's Two-Dimensional Model of Emotion ...... 28 Figure 4. The relation of valence and arousal...... 29 Figure 5. Relationships between psychological and physiological domains ...... 35 Figure 6. Illustrations of various phasically occurring physiological measures ...... 38 Figure 7. Optimal placement of EDA electrodes on palmar sites ...... 40 Figure 8. Schematic representation of facial musculature ...... 41 Figure 9. Suggested facial EMG electrode placement ...... 42 Figure 10. A typical ECG trace and the associated physiological events ...... 44 Figure 11. EEG traces during various mental states ...... 47 Figure 12. International 10‒20 System ...... 51 Figure 13. EDA during play and interviews ...... 55 Figure 14. Research stages...... 78 Figure 15. Screenshot of Left 4 Dead 2...... 94 Figure 16. Left 4 Dead 2: Tank and Witch boss enemies...... 97 Figure 17. Screenshot of Boredom condition (second iteration)...... 99 Figure 18. Screenshot of Balance condition...... 100 Figure 19. Screenshot of Overload condition...... 101 Figure 20. Screenshot of tutorial...... 103 Figure 21. Boredom condition (second iteration), feat. no combat...... 106 Figure 22. Screenshot of Sequencer main menu...... 110 Figure 23. Experimental laboratory ...... 118 Figure 24. Study 1: Experiment procedure...... 119 Figure 25. Study 1: Flow State Scale total flow results...... 122 Figure 26. Study 1: Flow State Scale subscale results ...... 122 Figure 27. Experimental procedure...... 135 Figure 28. EMG placement ...... 138 Figure 29. ECG placement...... 139 Figure 30. TestBench contact quality display...... 141 Figure 31. EEG placement...... 141 Figure 32. EDA placement...... 143 Figure 33. EMG data comparison ...... 143 Figure 34. Study 2 Short Flow State Scale results...... 152 Figure 35. IMI Interest/Enjoyment results...... 152 Figure 36. PENS competence results...... 153 Figure 37. PENS presence results...... 153 Figure 38. PENS autonomy results...... 153 Figure 39. EDA results ...... 157 Figure 40. HRV HF Peaks results ...... 157
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Figure 41. HR results...... 157 Figure 42. EEG frequency band results (reversed)...... 159 Figure 43. EMG OO results (reversed)...... 160
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List of Tables
Table 1. EEG frequency bands and associated states...... 49 Table 2. Overview of research discussed in Chapter 2 ...... 73 Table 3. Game condition differences (second iteration) ...... 101 Table 4. Game condition differences (third iteration) ...... 107 Table 5. Summary of main effect on subjective response ...... 151 Table 6. Summary of main effect on EDA, HR, and HF Peaks ...... 156 Table 7. Correlations between subjective and psychophysiological measures ...... 163 Table 8. Variances for all variables within conditions ...... 164 Table 9. Effect sizes for psychophysiological measures ...... 193 Table 10. Effect sizes for all subjective measures ...... 194 Table 11. Overview of significant results for subjective measures ...... 197 Table 12. Overview of significant results for psychophysiological measures ...... 201 Table 13. Overview of temporal costs for psychophysiological measures ...... 206 Table 14. Overview of utility of psychophysiological measurements ...... 208
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List of Abbreviations & Terms
AI Artificial Intelligence
ANS Autonomic nervous system
Arousal The intensity with which emotions are experienced
Baseline The natural or resting physiological state
BP Blood pressure
BPM (Heart) beats per minute
In which the challenge of the task does not outstrip the Challenge‒skill balance skill of the player
Corrugator supercilii, muscle located on the brow; CS generally, a measure of negatively valenced emotional expressivity
DDA Dynamic Difficulty Adjustment
In which one measure of arousal may decrease even as the Directional fractionation other increases
Electrocardiography: measurement of electrical changes ECG that occur during the heart’s contractions
Electrodermal activity: measurement of electrical activity EDA of the skin
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EEG Electroencephalography: measurement of cortical activity
Electromyography: measure of electrical activity of EMG muscles
Shooter game in which the camera perspective represents First-person shooter the player’s point of view
Flow Total absorption in an activity—the ‘optimal experience’
FSS-2 & S FSS-2 Long Flow State Scale-2 & Short Flow State Scale-2
GEQ Game Experience Questionnaire
Acclimatisation to a stimuli, such that physiological Habituation response diminishes
HCI Human‒Computer Interaction
High frequency; in HRV, this occurs between 0.15 and HF 0.4Hz
HR Heart rate: generally a measurement of beats per minute
Heart rate variability: measurement of the variation of HRV beat-to-beat intervals
Interbeat interval: period of time that occurs between each IBI heartbeat
IMI Intrinsic Motivation Inventory
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Low frequency; in HRV, this occurs between 0.04 and LF 0.15Hz
NPCs Non-player characters
Orbicularis oculi: muscle located near the eye; generally, a OO measure of positively valenced emotional expressivity
Physiological responses that occur when one is orienting Orienting response to a new stimulus or environment
PANAS Positive and Negative Affect Schedule
PENS Player Experience of Needs Satisfaction scale
Analysis of discrete physiological responses to specific Phasic events or stimuli
The character in the video game that the player is Player‒character controlling
PNS Parasympathetic nervous system
The experience of ‘feeling there’, being transported to the Presence mediated world
The study of relationships between psychological states Psychophysiology and physiological responses
REM sleep Rapid eye movement sleep
SAM Self-Assessment Manikin
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SNS Sympathetic nervous system
SPQ Spatial Presence Questionnaire
A pattern of physiological responses (e.g., reflexive Startle response blinking) that may occur in reaction to startling stimuli (e.g., a thunder crack)
A pattern of physiological responses that occur in reaction Stimulus-response specificity to specific stimuli
Ongoing analysis of physiological response to a stimuli; Tonic an 'average' of the experience
Valence Positivity or negativity of emotional experience
Digital game played on a computer, tablet, mobile phone Video game or dedicated gaming console (e.g., PlayStation 4)
Very low frequency; in HRV, this occurs between 0 and VLF 0.04Hz
Zygomaticus major: muscle located along the jaw; ZM generally, a measure of positively valenced emotional expressivity
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Statement of original authorship
The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution. To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.
Signature: QUT Verified Signature
Date: September 2017
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Acknowledgements
Ever since I came to the realisation that I was drawing near the end of my candidature, one concern has been at the forefront of my mind: how on earth can I possibly adequately acknowledge and thank the wonderful people who have supported me?
I’ll give it my best shot, but I may also be buying cupcakes.
I am exceedingly grateful to my principal supervisor, Daniel Johnson, for his continued reassurance during my moments of anxiousness (of which he is either excellent at sensing or I am terrible at hiding—probably both) and preternatural ability for guidance. He is an inspiration to me for his talents as a mentor, researcher, educator and answerer of last-minute panicked emails. I am also indebted to my associate supervisors: Peta Wyeth, for her encouragement, advice and keen insight into research approaches; and Simon Smith, who humoured my never-ending questions about all things psychophysiological with understanding and candour.
I would also like to thank my phenomenal colleagues at the Games Research and Interaction Design lab—in particular, Mitch, the stats wizard, for making every unit I taught with him a blast; Kellie, for her wit, cat photos, and encyclopaedic knowledge of research literature; and Nicole, for kindness, solidarity and yes, also her cat photos. Carody Culver, a big thank you for your copyediting and proof writing services within such a tight timeframe (and in accordance with the university-endorsed national ‘Guidelines for editing research theses’) – as well as the relief it’s brought me in scrolling through my New and Improved Thesis™ with its sense-making tenses.
I would also like to thank my friends and family. Cilla, Mei, Lyndon, Steph, Hoa, Martin, Alex, Lall, Vi: thank you for the games, the encouragement and the tolerance of my senseless 4 am messages. Thank you to my parents, Julie and Ray, for your support, cheerleading and encouragement in my endeavours in education (even as they never seemed to end).
Finally, thank you to my partner, best friend and cornerstone, Cody Phillips—who, as I write this, has just fallen asleep at 6.00am after keeping me company as I worked through the night. I’d promise to do the same for you come your thesis submission, but I really like sleeping.
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TABLE OF CONTENTS
1 Introduction ...... 1 1.1 Background ...... 1 1.2 Research Aim ...... 3 1.3 Contributions ...... 4 1.4 Significance and Scope ...... 4 1.5 Thesis Outline...... 6 2 Literature Review ...... 8 2.1 Scope ...... 8 2.2 Player Experience ...... 9 2.3 Flow and Optimal Psychology ...... 10 2.3.1 Challenge and Challenge‒Skill Manipulation ...... 13 2.4 Self-Determination Theory ...... 16 2.5 Presence ...... 17 2.6 Positive and Negative Affect ...... 19 2.7 Subjective Methods for Measuring the Player experience ...... 20 2.7.1 Survey Method ...... 20 2.7.2 Interviews and Focus Groups ...... 21 2.7.3 Ethnography, Observation and Think Aloud ...... 21 2.7.4 Game Metrics and Telemetry ...... 21 2.8 The Psychophysiological Method ...... 22 2.8.1 Background ...... 22 2.8.2 Benefits and Limitations...... 25 2.8.3 Recording ...... 26 2.8.4 Arousal and Valence ...... 27 2.8.5 Tonic and Phasic Measurement ...... 30 2.8.6 Psychophysiological Concepts ...... 32 2.8.7 Physiological Measures ...... 38 2.8.8 Limitations...... 51 2.9 Psychophysiology of the Player Experience ...... 52 2.9.1 Validating the Psychophysiological Method in Games ...... 53 2.9.2 Game Effects ...... 57
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2.9.3 Social Play ...... 62 2.9.4 Immersion ...... 64 2.9.5 Dynamic Difficulty Adjustment and Biofeedback ...... 66 2.9.6 Flow and Challenge ...... 68 2.9.7 Gaps Identified in Player Experience Literature ...... 72 3 Research Design and Methodologies ...... 74 3.1 Research Structure and Scope ...... 74 3.2 Research Stages ...... 77 3.2.1 Summary Stage 1—Development of Methodologies and Game Artefact ...... 78 3.2.2 Summary Stage 2—Pilot Testing and Design Iteration ...... 79 3.2.3 Summary Stage 3—Data Collection and Analysis ...... 80 3.2.4 Summary Stage 4—Evaluation ...... 81 3.3 Stage 1—Development of Methodology and Artefact ...... 82 3.3.1 Introduction ...... 82 3.3.2 Theoretical Grounding for Study Design ...... 83 3.3.3 Identifying a Viable Physiological Approach ...... 85 3.3.4 Selection of a Video Game Artefact ...... 93 3.3.5 Design Phase One ...... 95 3.3.6 Design Phase One: Artefact Design Flaws ...... 98 3.3.7 Design Phase Two ...... 98 3.3.8 Tutorial ...... 102 3.4 Stage 2—Study 1 (Pilot) and Revision ...... 104 3.4.1 Introduction ...... 104 3.4.2 Methodology ...... 104 3.4.3 Study ...... 105 3.4.4 Revisions to Methodology ...... 105 3.4.5 Development of Sequencing Software ...... 108 3.5 Stage 3—Study 2 ...... 111 3.5.1 Introduction ...... 111 3.5.2 Methodology ...... 111 3.6 Ethics and Limitations ...... 112 3.7 Stage 4—Analysis and Interpretation ...... 114
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3.7.1 Introduction ...... 114 3.7.2 Scope ...... 114 4 Study 1: Pilot ...... 116 4.1 Method ...... 116 4.1.1 Recruitment ...... 116 4.1.2 Measures ...... 117 4.1.3 Laboratory ...... 118 4.1.4 Procedure ...... 118 4.1.5 Participants ...... 120 4.2 Findings ...... 120 4.3 Discussion ...... 123 4.3.1 Difficulties in Reducing Flow in Immersive Games ...... 123 4.3.2 Unsuccessful Condition Design ...... 124 4.3.3 Challenge‒Skill as an Antecedent ...... 124 4.3.4 Scale Applicability ...... 125 4.4 Conclusions ...... 127 5 Study 2—Main Study ...... 128 5.1 Method ...... 129 5.1.1 Recruitment ...... 129 5.1.2 Self-Report Measures ...... 130 5.1.3 Demographics Questionnaire ...... 130 5.1.4 Short Flow State Scale ...... 130 5.1.5 Player Experience of Needs Satisfaction ...... 130 5.1.6 Intrinsic Motivation Inventory ...... 132 5.1.7 Psychophysiological Measures ...... 132 5.1.8 Ethics ...... 133 5.1.9 Procedure ...... 133 5.1.10 Attachment of Psychophysiological Measures ...... 135 5.1.11 Electrodermal Activity Attachment ...... 136 5.1.12 EMG Attachment ...... 137 5.1.13 Electrocardiography Attachment ...... 138 5.1.14 Electroencephalography Attachment ...... 140
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5.1.15 Data Treatment ...... 141 5.1.16 Electrodermal Activity ...... 142 5.1.17 EMG OO and CS ...... 143 5.1.18 Electrocardiography ...... 144 5.1.19 EEG ...... 145 5.1.20 Participants ...... 146 5.1.21 Analysis ...... 147 5.2 Self-Report Results ...... 148 5.2.1 Confirmation of Optimal and Sub-optimal Conditions ...... 148 5.3 Psychophysiological Differences in Optimal and Sub-Optimal Conditions ...... 154 5.3.1 Assumptions and Outliers for Psychophysiological Measures ...... 154 5.3.2 Results for Electrodermal Activity, High Frequency and Heart Rate ...... 155 5.3.3 Results for Electroencephalography ...... 158 5.3.4 Results for Electromyography - Orbicularis Oculi ...... 159 5.3.5 Results for Electromyography - Corrugator Supercilii ...... 161 5.3.6 Results for Exploration of Predictive Relationships ...... 161 5.4 Discussion ...... 165 5.4.1 Subjective Experience of Play ...... 165 5.4.2 IMI Interest/Enjoyment ...... 165 5.4.3 Flow ...... 167 5.4.4 Player Experience of Needs Satisfaction Competence ...... 170 5.4.5 Player Experience of Needs Satisfaction Autonomy ...... 172 5.4.6 Player Experience of Needs Satisfaction Presence ...... 173 5.4.7 Confirmation of Condition Design Success ...... 174 5.5 Psychophysiological Response to Play ...... 175 5.5.1 Electrodermal Activity ...... 175 5.5.2 Electrocardiography—Heart Rate ...... 179 5.5.3 Electrocardiography Heart Rate Variability (High-frequency Peaks) ...... 182 5.5.4 Electromyography Orbicularis Oculi ...... 184 5.5.5 Electromyography Corrugator Supercilii ...... 186 5.5.6 Electroencephalography ...... 187 5.6 Effect Sizes ...... 193
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6 Summary and Conclusions ...... 195 6.1 Self-Report Summary ...... 195 6.2 Psychophysiology Summary ...... 198 6.3 Exploration of Research Questions and Aim ...... 202 6.4 Applicability of Psychophysiological Assessment ...... 204 6.4.1 Time Costs ...... 204 6.4.2 Viable Psychophysiological Approach ...... 207 6.4.3 Sample Sizes ...... 212 6.4.4 Data Quality Checks ...... 213 6.4.5 Automation and Reduction of Participant‒Researcher Interaction ...... 213 6.4.6 Familiarisation with Psychophysiological Principles ...... 214 6.4.7 Summary ...... 214 6.5 Limitations and Future Research ...... 215 6.5.1 Future Approaches to Analysis ...... 222 6.6 Contributions to Knowledge ...... 224 6.7 Conclusion ...... 227 6.8 Final Comments ...... 230 7 References ...... 231 8 Appendices ...... 243 8.1 Appendix A—FSS Sample Questions ...... 243 8.2 Appendix B—Example PENS Items ...... 244 8.3 Appendix C—IMI Interest/Enjoyment Subscale Items ...... 245 8.4 Appendix D—Demographics Questionnaire ...... 246 8.5 Appendix E—Study 2 Script ...... 247 8.6 Appendix F—Electroencephalography EEG Outliers ...... 251 8.7 Appendix G—Boredom, Balance, and Overload Play Condition Videos ...... 254
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1 INTRODUCTION
1.1 BACKGROUND
Over the past 15 years, the perception of video games and games culture has transformed dramatically; while concerns about the worth and potential harms of video game play were originally the central focus of games research, contemporary research increasingly investigates the merits of gameplay and the possibilities for promoting positive player experiences (Brand &
Todhunter, 2015; Nacke et al., 2009). As video games have emerged as a leading form of entertainment, with more than nine in 10 Australian households possessing a gameplay device
(Brand & Todhunter, 2015), so too has the need to explore, evaluate and understand player experiences.
To accommodate this need, player experience research has become a critical component of video game evaluation within academic contexts. Founded on user experience, player experience research allows for the analysis and measurement of video game experiences with a focus on understanding the relationship and interactions between players and video games (Nacke et al., 2009). Conceptualising the player experience is undertaken through the lens of psychological constructs, as informed by psychology and behavioural sciences (Nacke et al.,
2009); these constructs include challenge, flow, immersion/presence, competence, tension and emotions (Wiemeyer, Nacke, Moser, & Mueller, 2016).
In particular, challenge and challenge‒skill balance (wherein the challenges of the game are adequately matched by the skills of the player) have been identified, and are widely recognised, as important elements in ensuring optimal player experiences (Csikszentmihalyi, 1990;
Przybylski, Rigby, and Ryan, 2010; Sweetser & Wyeth, 2005; Fong, Zaleski, & Leach, 2014).
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Challenge has been established as a key component of games intended to entertain, with player experience research revealing that players seek, and are driven by, challenge (Andrade, Ramalho,
Gomes, & Corruble, 2006; Lomas, Patel, Forlizzi, & Koedinger, 2013). Furthermore, challenge‒ skill balance has been identified as a probable antecedent to flow, a positive psychology concept of optimal experience lauded as integral to the development of successful player experiences
(Nakamura & Csikszentmihalyi, 2002; Chen, 2007).
Using a wide range of methods to evaluate the player experience is essential to expanding understanding of play components such as challenge. Contemporary player experience research employs a myriad of assessment methods informed by HCI and psychological research contexts, as well as by commercial games testing spaces (Nacke, Drachen, & Göbel, 2010). Within player experience research, methodologies have been adapted to assess the unique characteristics of gameplay (Lankoski & Björk, 2015). In particular, psychophysiological assessment has been identified as a valuable method for player experience evaluation, and has been employed in a variety of studies within the player experience domain (Kivikangas et al., 2011). As psychophysiology offers direct insight into the emotional experience of the player through the measurement of physiological responses, the method’s relevance to player experience research is substantial (Nacke, 2013).
Despite the growing popularity of psychophysiological assessment, physiological responses to player experiences are not yet well understood. While some extant literature has provided general conclusions about these responses, such as the association of physiological indicators of mental stress with challenge‒skill balance and flow (Drachen, Nacke, Yannakakis,
& Pedersen, 2010; Keller et al., 2011), other findings have challenged this (Kivikangas, 2006).
Psychophysiological evaluation of like concepts in player experience literature is often mediated
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by small sample sizes, a lack of uniformity across methodologies and experimental design, or limitations in the consideration of psychophysiological concepts.
Further psychophysiological research addressing these limitations stands to enhance understanding of the player experience. Opportunity exists for a rigorous psychophysiological evaluation of player experience constructs, as informed by psychophysiological concepts and gaps in player experience literature. Coherent employment of psychophysiological measurements alongside subjective measurements has been identified as a beneficial approach for the in-depth analysis of video games (Bernhaupt et al., 2008), and would allow for novel knowledge contributions to the psychophysiological understanding of the player experience in terms of both physiological results and the use of psychophysiological measures.
1.2 RESEARCH AIM
This program of research seeks to expand upon current understanding of the psychophysiological experience of play, as well as the utility and value of psychophysiological measures as a means of evaluating the player experience. To achieve this, the research undertaken for this thesis employed a large-scale psychophysiological approach to assessing the player experience using a variety of psychophysiological measures, a large sample size and a robust and coherent set of play conditions that allowed for the useful comparison of psychological and psychophysiological experiences. The research methodology and interpretation of results were guided by principles established within the psychophysiology field.
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1.3 CONTRIBUTIONS
The research reported in this thesis specifically investigates constructs of play identified as critical in promoting optimal player experiences. It is hoped that the research methodologies and results represent a novel contribution to player experience research.
In particular, the psychophysiological assessment of critical game constructs can provide games researchers, game developers and game players with a more nuanced understanding of optimal play experiences. This will allow for more informed evaluation, and therefore development, of play experiences. These findings may assist burgeoning research investigating the practical applicability of psychophysiology in spaces such a biofeedback and dynamic difficulty adjustment (DDA). As psychological constructs employed within games research are not exclusive to player experience analysis, the results reported here may also prove beneficial to researchers in other areas, such as user experience, psychology, psychophysiology and pedagogy.
1.4 SIGNIFICANCE AND SCOPE
This research commences with a broad investigation of extant player experience literature, exploring both critical psychological components of the player experience and the employment of psychophysiological evaluation to assess these components. A review of psychophysiological methodologies, measures and principles is also presented. This literature informed the first step in experimental design through the iterative development of video game conditions designed to act as a useful means of comparison for psychophysiological results.
An initial study (Study 1) further informed both additional iterations for the game condition and the experimental design, featuring the introduction of assistive programs for data
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collection. Upon finalisation of the game conditions and experimental design, the large-scale final study (Study 2) commenced.
The data gathered from the final study is treated, analysed and interpreted here in accordance with both player experience and psychophysiological practices. Several recommendations are then made for the ongoing employment of psychophysiological evaluation in player experience spaces.
This program of research represents one of the first steps in player experience research towards the contemporaneous employment of both a wide array of psychophysiological measures and subjective measures within a large sample size. The results are expected to expand on understanding of the psychophysiological experience of play, as well as the employment of psychophysiological evaluation within player experience research.
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1.5 THESIS OUTLINE
Chapter 2: Literature Review provides an overview of relevant research into both player experience constructs and psychophysiological assessment. Constructs such as flow, challenge, presence, self-determination theory (SDT) and enjoyment are investigated, and background in psychophysiology and psychophysiological principles is provided. A synthesis of literature employing psychophysiological measures in player experience evaluation is undertaken, allowing for the identification of gaps in this space; these gaps inform the methodological approach for this program of research.
Chapter 3: Methodology outlines the research aims and questions developed based on gaps identified in the literature review. This chapter also details the path taken in experimental design, discussing the video game conditions, assistive experiment software and chosen psychophysiological approach; it also gives an overview of both Study 1 and Study 2.
Chapter 4: Study 1 describes the initial non-psychophysiological study undertaken to review the suitability of the game conditions in the assessment of flow. The results inform several iterations for the game conditions, methodology and experimental design, and contribute novel findings about the assessment of optimal and sub-optimal player experiences. Most notably, the program of research is expanded here to include the investigation of additional player experience constructs.
Chapter 5: Study 2 details the final large-scale psychophysiological study undertaken to evaluate optimal and sub-optimal player experiences. Results from both subjective measures
(flow, interest/enjoyment, competence, autonomy and presence) and psychophysiological measures (EDA, HR, HF Peaks, EMG orbicularis oculi [OO], EMG CS and EEG) are reported and interpreted.
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Chapter 6: Summaries and Conclusions provides a summative discussion of the findings from Study 2, and examines the value and utility of psychophysiology in evaluation of player experience analysis (as informed by this research). This chapter also details the limitations and contributions of this program of research, and proposes recommendations for future research.
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2 LITERATURE REVIEW
2.1 SCOPE
The objective of the literature review in this chapter is to establish understanding of the player experience of digital games. The literature review concentrates on the review and exploration of evaluation methods that are employed within player experience research, with particular focus on the use of psychophysiological assessment.
The chapter begins by broadly exploring the current understanding of the player experience, and how this is differentiated from the more general ‘user experience’ of non-entertainment technologies. Key to the player experience are the various subjective psychological constructs associated with the experience play; the more prominent of these constructs, in terms of their breadth of study within current literature, are explored and defined here. Additionally, the chapter identifies measures and models by which these constructs are commonly assessed.
This chapter thus provides a basis for understanding psychophysiology and psychophysiological assessment through exploring various psychophysiological instruments, their capacities and the application of the psychophysiological method to player experience analysis.
Relationships between psychophysiological and psychological domains are also explored.
Finally, the chapter discusses methodologies used in prior psychophysiological games research and identifies gaps in the psychophysiological understanding of the player experience.
These gaps inform the methodologies, aims and research questions of the studies undertaken within the candidature, as discussed in Chapter 3.
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2.2 PLAYER EXPERIENCE
Video games represent the fastest growing leisure market in the world (Chatfield, 2010); in
Australia, video games are played by over 68% of the population (Brand & Todhunter, 2015).
Furthermore, 90% of Australian households possess a device used for playing video games (Brand
& Todhunter, 2015). However, despite their popularity, the appeal of video games is deceptively difficult to explain (Boyle et al., 2012).
The player experience of video games is similar to the standard user experience of software technology, in that it is defined by what the interaction feels like to the user (Preece et al., 2002)— specifically in relation to pleasure, fun, enjoyment and aesthetics. Measuring fun is inherently challenging, as ‘fun’ is not easily quantifiable; this is dissimilar to measuring a program’s usability, in which the interaction can be determined in terms of efficiency and time (Preece et al.,
2002). Despite its challenges, measuring and defining fun in video game play remains a locus of player experience research. In an investigation of a uses and gratifications paradigm for video game play, Sherry et al. (2006) found six dominant dimensions of motivation: arousal (the stimulation of emotions as a consequence of action), challenge, competition, diversion (the avoidance of stress or responsibilities), and fantasy (escapism and the ability to do things not feasible in reality), and social interaction. While these dimensions may not necessarily neatly equate to a concept of ‘fun’, they do represent a noteworthy step towards understanding and evaluating video game enjoyment and engagement.
As fun is a defining aspect of the video game player experience, the evaluation or measurement of fun is especially important. User experience in the context of video games primarily deals with the user’s emotional response while they interact with the play technology; for clarity, this thesis refers to the user experience of video games as the ‘player experience’, a
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term widely used in digital games research literature (Nacke et al., 2009). Wiemeyer, Nacke,
Moser and Mueller (2016) describe player experience as ‘the qualities of the player-game interactions … typically investigated during and after the interaction with games’. These experiences are often formally explored within player experience research as constructs of flow, challenge‒skill balance, affect, presence and motivation, all of which offer an avenue for investigation in this chapter.
Measuring the player experience can help explain the appeal of games, which can then facilitate a richer understanding of the medium and contribute to the development of more successful or appealing video games with improved player experiences. Furthermore, a more nuanced and informed understanding of psychological experience of play—and the methodologies used to explore this experience—will help to promote further understanding of motivational and engagement psychological phenomena.
2.3 FLOW AND OPTIMAL PSYCHOLOGY
Flow describes a mental state characterised by total absorption in and enjoyment of an activity (Csikszentmihalyi, 1990). Defined by Csikszentmihalyi (1990) as the ‘holistic sensation that people feel when they act with total involvement’, flow describes an optimal experience achieved in instances of challenge‒skill balance when engaged in an activity (Nakamura &
Csikszentmihalyi, 2002). For challenge‒skill balance to occur, the demands or challenges of the task must be met equally by the ability or skill of the individual. In the event of challenge outstripping skill, the activity becomes too difficult and prevents flow through the introduction of anxiety; in the event of skill outstripping challenge, the activity becomes too easy and disengages the individual through apathy or boredom (see Figure 1) (Csikszentmihalyi, 1990). An alternative, less adverse experience of skill outstripping challenge may also occur during relaxation—while
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still not allowing for a flow state to occur, relaxation does offer some of the merits associated with flow (Nakamura & Csikszentmihalyi, 2002). Beyond challenge‒skill balance, an additional requirement for flow is the presence of clear proximal goals and immediate feedback on the individual’s progress (Nakamura & Csikszentmihalyi, 2002).
Figure 1. The flow channel. Reproduced from Flow: The Psychology of the Optimal Experience (Csikszentmihalyi, 1990, p. 74).
Once these conditions have been met, Nakamura and Csikszentmihalyi (2002) designate the characteristics of flow as follows:
intense and focused concentration on the task at hand, or the activity taking place in the
present moment
a merging of action and awareness, in which one loses both the consciousness of the self
and awareness of everyday frustrations
loss of reflective self-consciousness—the absence of awareness of oneself as a social
actor, or the loss of concern for others’ perception of the individual
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a sense of control over the activity, and confidence in one’s capacity to deal with the task
at hand
an altered perception of time (distorted temporal experience), often in the sense that time
appears to pass more quickly
an autotelic experience—the experience of the activity at hand is intrinsically motivating
and self-rewarding, such that the activity itself is the reason for the effort expended.
Although the flow construct was originally applied to the experiences of athletes, artists and chess players conducting their respective tasks, flow has since been expanded to a wide variety of disciplines and areas, including medicine, education, wellbeing, social activism and writing
(Csikszentmihalyi, 1990; Nakamura & Csikszentmihalyi, 2002). Sherry (2004) has previously pointed to video games as possessing ideal characteristics for inducing and maintaining flow experiences. As the concepts of ‘enjoyment’ or ‘fun’ are difficult to define and quantify, the concept of flow provides a useful lens through which to evaluate the player’s experience of pleasure during gameplay.
Flow has been formally applied to games by Sweetser and Wyeth (2005) in the development of the GameFlow model: a set of guidelines, structured by flow, to aid in the design and experience evaluation of video games. The GameFlow model adapted the characteristics of flow— concentration, challenge, skills, control, clear goals and feedback—as well as the introduction of immersion and social interaction in creating a set of guidelines designed to enable or review the flow experience in video game play. The GameFlow model allowed for Sweetser and Wyeth to accurately distinguish between high- and low-rated games, and furthermore, to identify the successes of the former and failings of the latter.
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Cowley, Charles, Black and Hickey (2008) propose that video games are robustly able to invoke flow experiences, primarily as a consequence of low investment thresholds (the absence of financial investment or associated risks) and the opportunity for mastery within the game world.
In particular, Cowley et al. state that game mechanics ascribe to a general and familiar set of rules that allow for comfortable investment within the game world—in particular, ordered environments and opportunities for action, advancing in complexity as familiarity with the game also improves, facilitate ‘immediate access’ to the optimal experience.
Players value video games based on their ability to induce flow experiences (Chen, 2007).
The importance of designing for flow is realised within academic and commercial contexts; Chen states that ‘most of today’s video games deliberately include and leverage the eight components of Flow’. Chen proposes that games should try to maintain players within the ‘flow zone’ through careful manipulation of challenge‒skill balance—ensuring that the player remains within a state of challenge‒skill balance, and inhibiting the potential for boredom or anxiety to intrude upon the player experience. Due to the crucial role challenge-skill balance plays in evoking flow, this component represents one of the primary focuses of the program of research described within this thesis.
2.3.1 Challenge and Challenge‒Skill Manipulation
Much of game design is rooted in the concept that players seek, and are driven by, challenge (Lomas, Patel, Forlizzi, & Koedinger, 2013). In Andrade, Ramalho, Gomes and
Corruble’s (2006) survey of the main features of entertaining games, challenge was reported as of key importance. Successfully completing challenging tasks in game environments generates a sense of greater self-efficacy and accomplishment for players (Lomas et al., 2013). Research also suggests that simply undertaking optimally challenging activities—rather than just the experience
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of succeeding at the task—is an enjoyable experience in itself (Csikszentmihalyi, 1990), which establishes challenge-based game play as an intrinsically motivating activity. The role of challenge‒skill balance in ensuring an optimal player experience is further supported by
Przybylski, Rigby and Ryan (2010), who describe challenge‒skill balance as a critical element in the design and success of arcade games:
The pacing of challenges was designed so players could continually experience enhanced
competence as they progressed in the game, with challenges increasing apace with player
ability. This balancing of game difficulty and player skill was critical to the success of
arcade games; if the challenges underwhelmed players, they would lead to boredom, and if
they overwhelmed the player, they would generate frustration.
The importance of challenge‒skill balance to inducing flow is supported by the research of Rheinberg and Vollmeyer (2003), who created three game conditions within Roboguard, a video game in which the player must steer a spaceship out of the path of hostile rockets; these game conditions varied according to task demands defined by the speed of the attacking rockets
(easy, medium and hard). In alignment with flow literature, the ‘medium’ difficulty was rated highest for experiences of flow.
Research by Keller and Bless (2008) investigated the experience of flow through the manipulation of challenge‒skill balance within modified versions of the video game Tetris. In the program’s first study, three different versions of Tetris were developed: an adaptive condition in which the difficulty level of Tetris dynamically updated to meet the player’s skill level (achieving optimal challenge‒skill balance); an overload condition with very high task demands; and a boredom condition with low task demands. Consistent with the elements of flow, participants in the adaptive condition reported an altered perception of time (which felt accelerated), greater involvement and enjoyment, and greater perceived fit of skill and task demands (Keller & Bless,
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2008). This simultaneously emphasises the crucial role of challenge‒skill balance in seeding the impetus for flow, and the role flow plays in video game enjoyment.
Despite the importance of challenge and challenge‒skill balance in evoking an optimal play experience, the notion of challenge is not yet well-defined within games literature (Cox,
Cairns, Shah, & Caroll, 2012). The experience of challenge has been found to depend on both game genre (Cole, Cairns, & Gillies, 2015) and the relationship of the player with the game or gaming in general (Alexander, Sear, & Oikonomou, 2013; Lomas et al., 2013). Research investigating engagement in an educational game found that players were more engaged, and played longer, when the game presented an easy challenge as opposed to a moderate challenge
(Lomas et al., 2013), complicating the role of challenge‒skill balance as crucial to creating optimal player experiences. Lomas et al. suggest that a possible explanation for this outcome was the lack of prior experience among their sample, and the possibility that challenge-seeking behaviours may only occur after some level of expertise is acquired. This is supported by other research that discovered ‘casual’ players of a game gained more enjoyment from easier difficulties, regardless of their aptitude in the game, whereas ‘experienced’ players’ game enjoyment was predicated upon challenge (Alexander et al., 2013). This finding has been reflected in the evaluation of flow experiences, in which increased challenge in game tasks was found to both induce greater flow experiences for highly skilled players, and reduce flow experiences for low- and moderately- skilled players (Jin, 2011). Cox et al. (2012) suggest that one mediator of this experience may be the player’s self-perception: if they do not perceive themselves as possessing the requisite expertise for overcoming the game challenges, the positive experience of the game—explored as immersion within their research—will be reduced. It is possible that these findings, explored further, may provide an explanation for some subgroups’ (e.g. older players) reticence to engage in certain video game contexts, such as online multiplayer.
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These findings—the effects of perceived challenge, and variance of player skill—have prompted research questioning the centrality of challenge‒skill balance in invoking flow experiences. In a meta-analysis of 28 studies, Fong, Zaleski and Leach (2014) investigated the role of challenge‒skill balance in both flow and intrinsic motivation within a variety of contexts.
Their results revealed a moderate relationship between challenge‒skill balance and flow, but the authors maintain that challenge‒skill balance remains a ‘robust contributor’ to flow alongside
‘clear goals’ and ‘sense of control’ (the other antecedents of flow identified by Csikszentmihalyi
[1990]). Furthermore, Fong et al. suggest that the difficulty of operationalising challenge may explain the ‘moderate’ result. Ultimately, the authors conclude that challenge‒skill balance remains robustly related to feelings of flow or optimal experience.
2.4 SELF-DETERMINATION THEORY
Motivation and need satisfaction in video games has been understood through the application of SDT, an established psychological theory of motivation concerned with the fulfilment of a universal need to experience competence, relatedness and autonomy. SDT primarily addresses factors that enable intrinsic motivation, which is a core reason for involvement in play and sport (Ryan, Rigby, & Przybylski, 2006). As self-determination theory is not one of the core focuses of this thesis, but instead peripheral to interpreting results, this section will be feature broader—rather than in-detailed—discussion for the benefit of contextualisation.
Autonomy refers to the sense of volition or willingness experienced when doing a task
(Deci & Ryan, 2000), and is positively associated with increased intrinsic motivation. Enabling a sense of freedom and choice for players, providing opportunities that interest them and avoiding factors that diminish volition (such as controlling or limiting tasks) promotes autonomy, in turn promoting willingness to engage in the activity (Ryan et al., 2006).
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Competence addresses feelings of effectiveness and a need for challenge within the game
(Ryan et al., 2006). To achieve satisfied competence, players should be optimally challenged, receive positive feedback and be presented with the opportunity to improve on their abilities and skills.
Relatedness investigates feelings of connection to others. Within gaming, this primarily pertains to experiences of relatedness to human others in multiplayer games; however, this may also address the sense of connection to computer-controlled non-player characters (NPCs) (Ryan et al., 2006). Research conducted by Coulson, Barnett, Ferguson and Gould (2012) offers credence to the latter concept with its finding that players form ‘authentic emotional attachments’ to NPCs that arise as a consequence of NPC appearance, friendliness and utility.
Studies of SDT in video game contexts have revealed less competence and autonomy when players engage in co-located play with others than in solo play or online play (Johnson,
Wyeth, Clark, & Watling, 2015), autonomy, competence and relatedness as positive predictors of game enjoyment and future play, and positive associations between autonomy and competence and post-play mood (Ryan et al., 2006).
2.5 PRESENCE
Within player experience research, presence is often divided into two forms: social presence, or the feeling of being with another person within a mediated world (for example, within a chatroom) (Biocca, Harms, & Burgoon, 2003), and spatial presence, which describes the experience of being physically located within the mediated world (Wirth et al., 2007). Spatial presence, facilitated by engaging narrative and visually pleasing aesthetics, is characterised by the
International Society of Presence Research as ‘[occurring] when part or all of a person’s
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perception fails to accurately acknowledge the role of technology that makes it appear that s/he is in a physical location and environment different from her/his actual location and environment in the physical world’ (International Society for Presence Research, 2000). As with the discussion of
Self-Determination Theory in the previous section, the following discussion of presence will only be broadly discussed.
The concept of presence is often conflated with the concept of immersion within player experience research. Vella (2016) distinguishes between the two constructs, establishing that while presence relates more to the sense of physical location within the game world, immersion describes engagement over time (for example, emotional involvement in the game narrative).
Vella further suggests that game experiences lacking engaging game worlds, such as Tetris, may be simultaneously capable of inhibiting presence while evoking immersion, whereas games that do feature game worlds, such as BioShock (a dystopian first-person shooter featuring realistic and well-crafted environments), are capable of promoting both experiences.
In player experience assessment, the PENS scale evaluates both immersion and presence as a single construct comprising items measuring physical, emotional and narrative presence. The conflation of these constructs is undertaken as an investigation of the ‘illusion of non-mediation’, wherein the player perceives themselves as engaged and present within a mediated world
(Lombard & Ditton, 1997).
Presence is positively related to space exploration and the need for discovery within game worlds (Skalski, Dalisay, Kushin, & Liu, 2012), and has been identified as a key contributor to greater enjoyment of games (Przybylski et al., 2011; Lombard & Ditton, 1997). Games played at higher difficulties (that is, games that have not been identified as ‘easy’) or in first-person view,
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or that feature realistic environments, have also been identified as more likely to provoke feelings of presence (Ravaja et al., 2004).
2.6 POSITIVE AND NEGATIVE AFFECT
Affect is an experiential phenomena concerned with state feelings or emotions. One of the broadest measures of the player experience, affect evaluates the player’s positive or negative feelings. There is some ambiguity in the literature concerning ‘mood’ and ‘affect’—while the terms are often used interchangeably, such as in Ravaja et al.’s study of phasic emotional responses
(Ravaja, Saari, Salminen, Laarni, & Kallinen, 2006a), an important distinction exists between the two. While affective states are emotions experienced during an event (or, in the context of this research topic, within player experience), mood is a lasting disposition that may affect a person’s overall perception of an event (Sims, 1988). The glossary of the American Psychiatric Association states that ‘affect is momentary (like weather), while, mood is a prolonged emotion (like climate)’; in the context of human emotion, “affect” may be a transitory or reactionary emotion (e.g. surprise), and mood a state emotion (e.g., happiness or contentment). Within the context of player experience evaluation, it is necessary to measure affective states, as these are the emotions experienced during play (Mandryk & Atkins, 2007). As with the preceding discussions of Self-
Determination Theory and presence, the following section will again approach this concept broadly.
Positive affect has been linked with positive player experiences, such as competence satisfaction (Ryan et al., 2006), convergence between the player’s experiences of self and the player’s ideal self during play (Przybylski, Weinstein, Murayama, Lynch, & Ryan, 2012) and prosocial play (Saleem, Anderson, & Gentile, 2012). Conversely, a study by Jennett et al. (2008) examined the relationship between fast-paced games, negative affect and state anxiety; while they
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found that negative affect and state anxiety were higher for faster paced games than slower games, the results were not significant. Negative affect and emotions have thus been recommended for further study (Jennett et al., 2008; Boyle, Connolly, Hainey, & Boyle, 2012).
2.7 SUBJECTIVE METHODS FOR MEASURING THE PLAYER EXPERIENCE
Player experience research employs a number of methods for experience evaluation, informed by both HCI research contexts and commercial games testing (Nacke, Drachen, &
Göbel, 2010). In commercial spaces, play experience evaluation has historically been informal and performed with in-house testers; however, contemporary commercial play-testing employs formalised strategies from HCI research and usability fields (Nacke, Drachen, & Göbel, 2010).
Within player experience research, research methodologies have been adapted for assessing the unique characteristics of gameplay (Lankoski & Björk, 2015). Several evaluation methods used for player experience research within both academic and commercial contexts are discussed in this section.
2.7.1 Survey Method
As a form of structured interview, surveys are one of the most frequently used research methods for subjectively measuring the player experience (Cote & Raz, 2015). The widespread use of surveys in player experience research is partly owed to their ease of distribution and flexible design. In industry and research settings, a post-play questionnaire can be delivered to the participant sample, allowing for a mix of quantitative and qualitative responses relating to the play experience. Rich qualitative data about the player experience can be captured through open-ended questions, which offer insight into the broad themes and trends that emerge (Cote & Raz, 2015).
Quantitative data is typically captured through the inclusion of Likert scale items, which limit the scope of participant responses, since participant experiences are numerically signified by their
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level of agreement with a statement. In the context of player experience evaluation, constructs relating to player enjoyment are particularly examined (Nacke, Drachen, & Göbel, 2010).
2.7.2 Interviews and Focus Groups
Like the survey method, interviews and focus groups allow for the collection of subjective player experience data. Unlike the survey method, however, interviews and focus groups centre on the collection of qualitative data, are typically semi-structured and make extensive use of open- ended questions, thus allowing researchers to probe participants’ responses for a more detailed and nuanced understanding of player experience phenomena (Cote & Raz, 2015). Qualitative data collected through interviews and focus groups are often used to triangulate experiential phenomena between multiple data streams in mixed-methods research (Cote & Raz, 2015).
2.7.3 Ethnography, Observation and Think Aloud
If participants’ in-game behaviour forms part of the research question, an ethnographic- or observation-driven approach to examining the phenomena may occur. Observing participants as they play a game allows for insight into the player experience that might otherwise be uncaptured by the game’s telemetry—for example, player behaviours or participants’ cognitive load (Lieberoth & Roepstorff, 2015). A limitation of this approach is that it relies on the researcher’s subjective observation of events about the player’s interaction with the game.
Observing participants in laboratory settings is often coupled with a think-aloud exercise, so that participants vocalise their thought process, giving insight into what guides their behaviour.
2.7.4 Game Metrics and Telemetry
The digital nature of video games allows for the collection of vast amounts of instrumentation data. By logging and tracking in-game events such as input commands, interactions with entities, movement through the game space and so on, researchers are able to
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determine the types of interactions that occur in the game (Nacke, Drachen, & Göbel, 2010). While this approach yields valuable insight into the events taking place during play, a core limitation of this method is that interpreting game metrics is difficult and largely requires subjective interpretation of user stories, as well as the development of player personas to provide structure to the data investigation (Tychsen & Canossa, 2008).
2.8 THE PSYCHOPHYSIOLOGICAL METHOD
‘While game metrics provide excellent methods and techniques to infer behavior from the interaction of the player in the virtual game world, they cannot infer or see emotional signals of a player.’ (Nacke, 2013, p. 585)
The psychophysiological method allows for the covert, direct interpretation of player emotional response to play experiences through physiological measurement (Nacke, 2013). The coherent employment of psychophysiological measurements alongside subjective measures has been identified as a beneficial approach for in-depth analysis of the player experience (Bernhaupt et al., 2008), and has recently enjoyed growing popularity within player experience research and industry. The following section explores the psychophysiological method, constructs and measures, and seeks to establish a broad understanding of psychophysiology for employment in player experience evaluation.
2.8.1 Background
Andreassi (2007, p. 2) defines the field of psychophysiology as ‘the study of relations between psychological manipulations and resulting physiological responses, measured in the living organism, to promote understanding of the relation between mental and bodily processes’.
By this definition, ‘mental processes’ encompass both emotional responses (such as fear, anger or
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joy) and cognitive processing (such as problem-solving); the use of psychophysiological instruments allows for a researcher to record and identify physiological responses associated with these behaviours. These physiological responses are sourced from a multitude of sites on or within the body. A characteristic example of a physiological response to emotion, for example, is an increased heart rate (HR) associated with stress or fear; similarly, problem-solving is also associated with an increase in cognitive activity, as recorded by sensors monitoring the brain
(Melillo, Bracale, & Pecchia, 2011; Stern, Ray, & Quigley, 2001).
Figure 2. Divisions of the nervous system.
. Psychophysiological signals originate as electrochemical changes in neurons, muscles and gland cells (Stern et al., 2001, p. 33), as controlled by the nervous system (see Figure 2). The nervous system is divisible into the central nervous system (CNS), which consists of the brain and
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spinal cord, and the peripheral nervous system, which consists of nerves and ganglia (nerve cell clusters) outside the brain and spinal cord (Andreassi, 2007, pp. 11‒12). Furthermore, the peripheral nervous system contains the autonomic nervous system (ANS), sub-divisible into the parasympathetic nervous system (PNS) and sympathetic nervous system (SNS), which are dominant in situations requiring rest and ‘work’ (that is, energy mobilisation), respectively. The
ANS is responsible for many of the physiological signals of interest in the field of psychophysiology—for example, eccrine sweat gland activity is controlled by the SNS (a sub- division of the ANS), and is a widespread measure of psychophysiological arousal (Andreassi,
2007, p. 13).
Unfiltered physiological signals are obtained through the placement of electrodes on human skin, the locational site of which depends on the specific psychophysiological measure being used. For example, a measure reliant on muscle activation—such as electromyography
(EMG)—requires that the electrodes be placed on the specific muscle site being investigated, such as the zygomaticus major (ZM: a cheek muscle associated with positive emotion). These signals are simply positive or negative voltage, characterised by amplitude, latency and frequency (Nacke,
2013). For analysis, the signals are then processed and filtered in accordance with the correct procedures for the specific measure chosen.
Psychophysiology has been used primarily in the fields of medicine, neurobiology, psychology and, most recently, HCI (Nacke, Grimshaw, & Lindley, 2010). Wastell and Newman
(1996) measured HR and blood pressure (BP) to determine the stress of British ambulance dispatchers in the switch from paper to digital systems; Ward and Marsden (2003) collected skin conductance, blood volume pulse and heart rates to gauge the user experience of navigating websites of varying design quality; and Wilson and Sasse (2000) evaluated psychophysiological
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response to video and audio degradations. The success and widespread adaptation of psychophysiological measures in other fields lends credibility to their use in a video game-specific context, particularly given their frequency in HCI research (Nacke et al., 2010b).
2.8.2 Benefits and Limitations
Whereas psychophysiology historically required the use of cumbersome equipment and manual recording, modern technological advances have allowed for relatively unobtrusive psychophysiological insight into the typical healthy user (Cacioppo, Tassinary, & Berntson, 2000, p. 3). As physiological responses are generally uncontrollable or unmodifiable by the participant, and occur spontaneously in response to stimuli, the psychophysiological method is an effective objective approach for experience analysis (Nacke, 2013). The continuous recording of physiological signals also allows for uninterrupted data collection, removing the necessity to pause an experiment for the distribution of a measure that requires active participant interaction.
Despite these advantages, the complexities associated with recording, treating and analysing psychophysiological data often present as a barrier to its use in non-clinical contexts.
The instruments themselves can often be invasive or uncomfortable for the participants, detracting from naturalised settings and environments. These obstacles are being resolved by recent developments in consumer-grade psychophysiological equipment, with the introduction of more intuitive and less conspicuous products such as the single-sensor MindWave EEG headset
(NeuroSky, 2015) and portable Empatica wristband (Empatica, 2017). However, research has shown that consumer-grade products often exhibit high variability in performance (Maskeliunas,
Damasevicius, Martisius, & Vasiljevas, 2016; Duvinage et al., 2013), suggesting that a middle ground may be preferable for academic and industry research.
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2.8.3 Recording
2.8.3.1 Electrodes and Preparation
To obtain the physiological response, electrodes are employed to collect the bioelectric potentials of the site—for example, muscle or gland—being recorded. This information is then relayed to a computer or polygraph, where the signal is filtered and amplified (Stern et al., 2001).
The data analysis then generally occurs as a separate event.
Typically, electrodes are placed on the surface of the skin (cutaneous electrodes), although subcutaneous or needle electrodes can also be used (Stern et al., 2001, p. 36). These cutaneous electrodes, in the form of metal discs, are placed in pairs to allow for a path to be established between each electrode. The electrodes are aided by a conductive gel or paste, of which the purposes are threefold: the gel or paste is often inserted into a socket between the electrode itself and the skin (providing a cushion that reduces risk of disturbances otherwise possible in direct electrode-to-skin contact, such as movement artefacts caused by small movements); it lowers the impedance between the electrode and the skin; and finally, the gel can act as an adhesive that helps affix and maintain the electrode to and in the correct position (Stern et al., 2001, p. 38). A plastic adhesive collar attached to the electrode also affixes the electrode to the skin, and often, tape is securely placed over the attached electrode to prevent detachment or slipping.
As the outer layer of skin is largely composed of dead skin cells, various oils and dirt, the site of electrode placement is prepared to ensure minimal impedance and an uncompromised signal. To do this, skin is often gently abraded until it becomes red with an abrasive cloth and/or skin preparation gel. The location is then wiped with alcohol and water to ensure a clean surface, except in the instance of electrodermal activity (EDA) (Stern et al., 2001, pp. 38–39). Finally, the
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resistance between the applied electrodes is checked with an impedance meter. An impedance of
< 10 kΩ is recommended (Stern et al., 2001, p. 39).
2.8.3.2 Filtering
The signal acquired by the electrodes is filtered during both amplification and pre-analysis to attenuate the frequencies, so that any data above or below a certain threshold is allowed to pass
(Stern et al., 2001, p. 41). For example, a low-pass filter may be set to discard any information received below 12 Hz; similarly, a high-pass filter may be set to discard any information above 12
Hz. This is useful for noise reduction, such as movement artefacts or interference from co-located physiological responses that occur at a greater or lower range than the target physiological measure.
A band-pass filter, a combination of both the high-pass and low-pass filters, allows for only a range of frequencies to pass. This is beneficial should the researcher or clinician be interested in a specific physiological response that only occurs within a certain range. For example, a beta wave collected in electroencephalography (EEG) is only present between 13 and 30 HZ— as such, a band-pass filter can attenuate any frequencies received above or below this range. A notch or band-reject filter is set to the frequency of the AC current in the country in which the information is collected, allowing for the attenuation of interfering frequencies (Stern et al., 2001, p. 41)—in Australia, it is set to 50 Hz in congruence with international voltage standards.
2.8.4 Arousal and Valence
Within the overlapping fields of player experience and emotional psychology, Russell’s dimensional theory of valence and arousal is particularly prominent (Mandryk et al., 2006b;
Nacke, 2013). Russell (1980) suggests that emotion is not rigid, and supports a dimensional theory
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of emotion: that all emotions can be located in a two-dimensional space, as coordinates of valence and arousal. Figure 3 provides an example of Russell’s circumplex model of affect.
Russell’s two-dimensional space of valence and arousal has been commonly used in psychophysiological studies (Mandryk et al., 2006a, 2006b & 2007; Nacke et al., 2010a & 2010b; and Lang, 1995). This is likely due to the limitations of psychophysiological responses as a measurement of explicit emotions, necessitating a reliance on valence and arousal feedback
(present in the axes of Russell’s model).
Figure 3. Russell's Two-Dimensional Model of Emotion (reproduced from Russell, 1980).
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Figure 4. The relation of valence and arousal (reproduced from Ambinder, 2011).
As psychophysiological instruments are unable to identify specific emotions (Stern et al.,
2001), psychophysiological evaluations of the player experience instead measure levels of valence and/or arousal. Valence (i.e., tone) reflects the positivity or negativity of an emotional experience—for example, whether the user is experiencing happiness or sadness. Arousal (i.e., activation) reflects the level, or extremity, of the valence—for example, high arousal and high valence could reflect intense happiness, whereas low valence, high arousal could reflect intense sadness (Ambinder, 2011; Nacke, 2010b; Ravaja, 2006a). A concise representational interplay of valence and arousal is provided in Figure 4. Psychophysiological analysis is often restricted to measurements of arousal and valence; as a result, multiple measurements are often used concurrently to aide in triangulating the correct emotion (Nacke, 2013). This is aided by the inclusion of survey and interview methods (Nacke, 2013).
Non-dimensional models of affect—that is, the categorical treatment of emotions as distinct and fundamentally discrete entities—are also of note. Ekman (1992) identifies six basic universal emotions, experienced to varying degrees of intensity: anger, fear, sadness, enjoyment,
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disgust, and surprise. While these emotions were identified in a cross-cultural examination of facial expression, Ekman (1992) also proposes that these emotions differ in behavioural and physiological response; furthermore, that these emotions each represent a ‘family’ of related states that share commonality in expression and physiology (e.g. there are different types of ‘anger’, but all share the commonality of constricted brow muscle movement or pursed lips).
It should be noted that physiological arousal does not simply extend on a unidimensional continuum ranging from low to high activation, as evident in the principle of stimulus-response specificity (Stern et al., 2001, p. 53; see section 2.8.6.4). Arousal can present in the cortical (e.g., alpha band EEG frequency), sympathetic (e.g., HR and BP) and somatic (e.g., muscle tension) systems (Bradley, 2000, p. 604), and as such, does not necessarily increase uniformly across all systems (Lacey, 1967). This is classically represented in the flight, fight or freeze responses, particularly concerning somatic response. Despite a high level of arousal, the somatic system may either incur a high level of activation (muscle activity in the flight or fight response) or a relatively low level of activation (as in the freeze response). (See section 2.8.6.4 for extrapolation on these concepts.)
2.8.5 Tonic and Phasic Measurement
Physiological activity is typically analysed in terms of two domains: tonic and phasic.
Tonic and phasic activity are the windows of physiological response that are investigated in psychophysiological research. (Stern et al., 2001, p. 47), and the type of activity analysed—tonic or phasic—depends on the aims of the study. Tonic and phasic psychophysiological approaches to player experience evaluation permit different perspectives of analysis. A tonic approach provides overall emotional feedback, allowing researchers to gain an overview or ‘average’ of the
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experience; alternatively, a phasic approach enables researchers to look at individual events (and their evoked responses) within an experience (Ravaja et al., 2006a; Mandryk et al., 2006b).
Tonic Activity: The purpose of tonic activity analysis is twofold. Tonic activity is considered to be the background, or baseline level, of activity (Stern et al., 2001, p. 49). It is thus collected over variable time intervals (from 30 seconds to several minutes, depending on the physiological measure being recorded) as a baseline prior to exposure to a stimulus. This baseline activity is recorded during participant inactivity, during which the participant is generally asked to relax. This allows for a useful point of comparison in analysis, enabling differentiation between the participant’s natural or resting physiological state and their reaction to stimulus, as in phasic analysis. The second purpose of tonic activity allows for ongoing analysis of continued exposure to a stimulus or experience. This generates an ‘average’ physiological experience associated with the stimulus—for example, a participant’s average HR throughout a 10-minute hazardous driving experience, compared with the participant’s average HR in a non-hazardous driving experience or when at rest.
Phasic Analysis: Phasic analysis allows for granular investigation of discrete physiological response to specific events or stimuli (Stern et al., 2001, p. 50). As in the hazardous driving example, phasic analysis in this scenario is useful for evaluating the participant’s immediate response to a specific hazard in the experiment condition (as opposed to the average response to the overall condition). This response may be an increase or decrease in amplitude or frequency of the response—for example, a five-second spike in skin conductance levels immediately following exposure to a stimulus.
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The method of psychological analysis is dependent on the aims of the research program.
Neither is phasic or tonic analysis mutually exclusive; both the average response and discrete responses can be useful when evaluating the psychophysiological experience.
2.8.6 Psychophysiological Concepts
In evaluating psychophysiological response, researchers should be aware of certain principles of psychophysiology that may influence interpretation of results (Stern et al., 2001, p.
52). Such an understanding will improve accuracy in data collection and analysis, and allow for informed and critical reading of extant psychophysiological literature within both psychophysiological and player experience research.
2.8.6.1 Habituation
Habituation is described by Stern (2001, p. 54) as a concept ‘as basic to psychophysiology as the concept of arousal’. Habituation describes the decrease in psychophysiological responsiveness that occurs with exposure—specifically, repeated exposure—to the same stimulus or set of stimuli (Andreassi, 2007, p. 353). Andreassi (2007, p. 353) provides the example of changes in EDA that occur when a person’s name is called; if the name is repeated over and over again, the novelty diminishes, and so too does the electrodermal response. Öhmann, Hamm &
Hugdahl et al. (2000, p. 540) describe exposure to a single intense stimulus, wherein electrodermal and HR components were reduced within 10 trials of repeated exposure to the stimulus. The presence of habituation is ubiquitous across a range of physiological measures, and may often be a function of repetition-induced boredom (Stern et al., 2001, p. 55; Andreassi et al., p. 353).
The presence of habituation is commonly observed in, and has implications for, both short-term and longitudinal psychophysiological assessment (Stern et al., 2001, p. 55). Habituated response can occur within a single testing session, but generally occurs slowly in response to
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particularly intense, complex or unique stimuli (Stern et al., 2001, p. 55); in terms of player experience research, games may present a complex enough stimulus to warrant this. Stern et al.
(2001, p. 55) also suggests that habituation may be minimised by interruption, either through the requirement of a response (e.g., a survey response) or the introduction of novel stimuli. Despite this, the ubiquity of habituation necessitates its consideration in experiment design and results interpretation.
2.8.6.2 Orienting
The orienting response describes a change in physiological response that occurs as a consequence of exposure to novel stimuli (Öhmann, Hamm, & Hugdahlm 2000, p. 542). Stern et al. (2001) detail some of the major components of orienting response as increased EDA, delayed respiration followed by an increase in frequency and amplitude and HR deceleration. Because the orienting response is sensitive to novel stimuli, it habituates rapidly after continued exposure to the stimuli (Stern et al., 2001); in player experience analysis, the orienting response may be circumvented with familiarisation of the game stimulus prior to data collection.
2.8.6.3 Startle Response
Startle response is the physiological response elicited by the abrupt introduction of an intense stimulus; Stern et al. (2001) provide the example of a sudden thunder strike. Reflexive eye blinking, HR acceleration and rapid habituation of the physiological response immediately following are typical of startle response. To minimise the risks of startle response influencing data,
Stern et al. (2001) recommend discarding data from initial exposure to a stimulus; the risk for unexpected external stimuli, such as a door slamming within the building, should also be minimised. As video games often feature the rapid introduction of confronting or intense stimuli, there is a potential for startle response in play experiences; Kivikangas (2006) identifies the chance
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of startle response influencing facial EMG results in their psychophysiological assessment of video game flow.
2.8.6.4 Stimulus-Response Specificity and Directional Fractionation
Physiological activation does not occur along a single unidimensional continuum. Lacey
(1967) proposes three complex forms of arousal: cortical, autonomic and behavioural.
Furthermore, Lacey states that one form of arousal cannot consistently be used as a valid measure of another. Stimulus-response specificity refers to a pattern of physiological response to a specific stimulus (Andreassi, 2007, p. 345). This pattern of physiological results may be interpretable as the pattern of response associated with specific emotional states (e.g., fear, disgust or happiness)
(Andreassi, 2007, p. 345). For example, Stern et al. (2001) provide the example of a missing wallet: noticing a missing wallet (or any missing valuable object) may prompt a specific pattern of physiological response, such as an increase in muscle tension alongside a decrease in heart and respiration rate.
This divergence in physiological response (a decrease in heart and respiration rate alongside an increase in muscle tension) is referred to as directional fractionation, and contradicts the notion that the ANS response must covary in a single direction (increase or decrease) in response to stimuli (Andreassi, 2007, p. 345). Stern et al. (2001) also give an example of a soldier on guard duty: upon hearing an unidentified noise, the soldier may experience an increase in cortical arousal, increase in EDA and a decrease in HR. Increased or decreased activation in one measure—otherwise interpreted as increased or decreased arousal—should thus not be considered reflective of the whole experience; in the case of the soldier, the interpretation of HR alone may not allow for the interpretation of a psychologically arousing experience.
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2.8.6.5 The Psychological and Physiological Domains
It is important to consider that the nature of psychophysiological response is such that a single physiological response is not necessarily singularly and uniquely related to a single psychological response, insofar as a single psychological state, or discrete emotional response, exists (Nacke, 2013). Cacioppo et al. (2000, p. 12) identify five possible relationships between the psychological and physiological domains (see Figure 5):
Figure 5. Relationships between psychological (ψ) and physiological (ϕ) domains. Reproduced from Handbook of Psychophysiology (Cacioppo, Bernston, & Tassinary, 2000, p. 12).
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One-to-One: A single physiological response is directly associated with a single psychological response, and vice versa. This allows the researcher to determine a specific mental process or psychological element based solely upon a physiological response. Such a relationship is rare.
One-to-Many: A one-to-many relationship, suggesting that a single psychological response is associated with a subset or multitude of physiological responses. This can often be simplified to a one-to-one relationship, as the identification of a particular set of physiological responses may be robustly associated with a single psychological response. In this instance, the numerous physiological responses are regarded as one set or group, or a single unified physiological response to a mental event. As with the one-to-one relationship, this is uncommon.
Many-to-One: The inverse of the one-to-many relationship. The many-to-one relationship is the association of a single physiological response with a multitude of psychological responses; this relationship is the one most often examined in psychophysiological research
(Nacke, 2013).
Many-to-Many: A many-to-many relationship suggests that two or more psychological responses are associated with same subset (multitude) of physiological responses; it thus does not allow for the inference of which psychological process the physiological signals may be responding to.
Null: No association between physiological and psychological response.
The first (one-to-one) and third (many-to-one) relationships are those most commonly examined in psychophysiological research, as they allow for an assumption about the psychological state to be made based on physiological response (Cacioppo et al., 2000, p. 12).
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Physiological responses are thus better understood as ‘elements of sets with fuzzy boundaries’
(Nacke, 2013).
The approach to emotion analysis discussed within this program of research, and within much psychophysiological literature, is that of an objectivist approach: the treatment of emotion as objective psychological concepts. However, an alternative constructivist approach is one that examines emotion as an interaction informed by and interpreted through cultural and social experiences (Boehner, DePaula, Dourish, & Sengers, 2007), which are not typically assessed in
HCI environments.
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2.8.7 Physiological Measures
The next section discusses the various physiological measures employed in psychophysiological research (see Figure 6). These measures record physiological activity from the central, peripheral, somatic and autonomic nervous systems through the use of cutaneous electrodes, infrared sensors and optical sensors. The general applications of these measures are then detailed.
Figure 6. Illustrations of various phasically occurring physiological measures. Reprinted from Handbook of Psychophysiology (Cacioppo, Bernston, & Tassinary, 2000, p. 706).
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2.8.7.1 Electrodermal Activity
EDA—previously known as galvanic skin response (GSR)—is one of the most widely used measures of psychophysiology, owing largely to its relative ease of deployment and quantification, as well as its sensitivity to psychological stimuli (Dawson, Schell, & Filion, 2000, p. 200). Primarily a measure of arousal, EDA is the study of electrical activity of the skin, as recorded by cutaneous electrodes. The activity is generated by an interaction between the SNS and local processes in the skin (Boucsein, 1992). Two fundamental methods are used for EDA: the exosomatic method and the endosomatic method. The exosomatic method records skin conductance and resistance levels, whereas the endosomatic method records skin potential response. As the exosomatic method is the one chiefly used in contemporary research (Dawson et al., 2000), this is the one discussed in this section.
Skin is a protective barrier that, among other functions, aids in bodily temperature control through follicle dilation and sweating. The measurement of EDA in psychophysiology is the investigation of the psychological states and processes that provoke sweating (Dawson et al.,
2000) through recording sweat gland activity originating in the dermis.
Within the dermis are two types of sweat glands: apocrine and eccrine. Apocrine sweat glands are large, terminate in hair follicles and are found in the genital and armpit areas (Andreassi,
2007, p. 315); while their role in human physiology is not well understood, they have been associated with the production of pheromones in animals (Dawson et al., 2000). Conversely, eccrine sweat glands are distributed generally over most of the human body surface and are found in their highest concentrations on the palms of the hands (palmar region) and the soles of the feet
(plantar region) (Andreassi, 2007, p. 316). While the primary function of most eccrine sweat glands is that of thermoregulation, those found on the palmar and plantar sites are more strongly
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associated with ‘grasping behaviour’ and are thus more responsive to emotional rather than thermal stimuli (Dawson et al., 2000). This is most noticeable in the phenomenon of ‘clammy hands’ in stressful or adverse contexts, regardless of the external temperature.
Despite its possible adaptive origins, increased EDA is not solely associated with low- valence emotions such as stress or anxiety. EDA is responsive to a breadth of stimuli, including
‘stimulus novelty, surprisingness, intensity, emotional content, and significance’ (Dawson et al.,
2000, p. 212). This stimuli sensitivity emphasises the necessity for a controlled experimental paradigm to restrict EDA response to the investigated variables (although spontaneous activity remains challenging to control against). The recommendation is thus to change only one aspect of a stimulus at a time (Dawson et al., 2000). The assistive use of alternative analysis methods, such as survey and interview, can also aid in determining the stimuli behind fluctuations in EDA.
For optimal recording of eccrine sweat gland activity, EDA electrodes are placed on either palmar or plantar sites. The recommended palmar sites are the medial phalanges, distal phalanges and hypothenar and thenar eminences of the palm (see Figure 7. Optimal placement of EDA electrodes on palmar Figure 7). The distal phalange site has been sites. Reprinted from Handbook of Psychophysiology (Cacioppo, Bernston, & Tassinary, 2000, p. 205). shown to have the greatest concentration of eccrine sweat glands, and is thus the
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recommended placement site for electrodes, unless experiment requirements or participant injury on the site prevents it (Dawson et al., 2000).
2.8.7.2 Electromyography
EMG measures the electrical potential of muscle activation (contraction) following neural stimulation (Stern et al., 2001), through either cutaneous electrodes or subcutaneous needle electrodes (the latter used more commonly in medical contexts) (Andreassi, 2007, p. 283). As with most psychophysiological methods, EMG can be employed to investigate a variety of topics, such as motor performance, cognition, sleep and emotional expression (Andreassi, 2007, p. 277)—the latter topic is the focus of this section.
Figure 8. Schematic representation of facial musculature. Reprinted from Handbook of Psychophysiology (Cacioppo, Bernston, & Tassinary, 2000, p. 706).
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In terms of emotional expression, EMG is valuable as a measure of valence. Like EDA,
EMG is highly sensitive to changes in the CNS as a consequence of psychological stimuli; this is evident in consistent patterning of specific facial muscle activation in emotional response through facial expression (Andreassi, 2007, p. 300). Positive and negative valence can be determined through the direct measurement of these specific muscles. One benefit of employing EMG to measure facial expression is that it is capable of detecting changes in emotional processes that are
‘too subtle or fleeting to produce observable facial expression changes’ (Andreassi, 2007, p. 303).
Both the ZM and OO have been used to index positively valenced emotions such as joy
and excitement through the muscle
activation that occurs during smiling
(Andreassi, 2007, pp. 300-303; Ravaja,
Turpeinen, Saari, Puttonen, & Keltikangas-
Järvinen, 2008; Witvliet & Vrana, 1995). In
the ZM, this is through the lip corners being
pulled up and back; in the OO, it is through
skin tightening that causes the lower eyelid
to rise (Tassinary & Cacioppo, 2001, p.
166). Conversely, the corrugator supercilii
Figure 9. Suggested facial EMG electrode placement. (CS) has been routinely correlated with Reprinted from Handbook of Psychophysiology (Cacioppo, Bernston, & Tassinary, 2000, p. 174). negatively valenced emotional expression,
such as anger and fear (Lang, Greenwald,
Bradly, & Hamm, 1993; Dimberg, 1990; Andreassi, 2007, pp. 300‒303) through the furrowing of the brow (Tassinary and Cacioppo, 2000, p. 166). For the exact location of these muscles, see
Figure 8.
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As facial muscles are striated (fibrous) and exist within close proximity of one another, the accurate determination of which muscles are contracting through cutaneous electrodes is challenging. Tassinary and Cacioppo (2001, p. 167) recommend referring to the muscle sites as regions; as opposed to ‘EMG activity in the CS’, it is more appropriate to refer to ‘EMG activity to the CS muscle region’. Consistent application of EMG electrodes is necessary to ensure an accurate signal from the correct muscle region (see Figure 9). As EMG activity is especially susceptible to particle and movement interference, electrode preparation and attachment must be undertaken carefully (Stern et al., 2001, pp. 112‒113).
2.8.7.3 Electrocardiography
Electrocardiography (ECG or EKG) is the measurement, through cutaneous electrodes, of the electrical changes that occur during the heart’s contractions. Although an autonomic activity, the frequencies, variations and pace of these contractions are responsive to the psychological stimuli of stressors, frustrations and fears (Andreassi, 2007, p. 438‒439; Melillo, Bracale, &
Pecchia, 2011), and cognitive processing (Allen, Obrist, Sherwood, & Crowell, 1987; Szabo &
Gauvin, 1992). A primary interest in cardiovascular psychophysiology is stress as it relates to heart disease.
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The electrical changes of heart contractions present on a normal ECG as a composite of
P, Q, R, S and T waves, each representing a discrete minute physiological change during the heartbeat (see Figure 10). The QRS complex is caused by ‘currents generated in the ventricles during depolarization just prior to ventricular contraction’ (Andreassi, 2007, p. 412), with the R wave its most prominent component. Due to its relative prominence, the R wave is the basis of many ECG analysis methods.
Figure 10. A typical ECG trace and the associated physiological events. Reproduced from Psychophysiological Recording (Stern, Ray, & Quigley, 2001, p. 181).
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Heart Rate and Interbeat Interval
The HR, commonly measured in beats per minute (BPM), is often used in psychophysiological research as a measure of increased arousal (Andreassi, 2007, p. 415). This is based on the number of occurrences over the preferred time window of the aforementioned R wave. An alternative method of approach is the interbeat interval (IBI), which measures the time that has passed between each R wave. Typically, IBI is employed in the investigation of a single cardiac cycle, whereas HR is preferred for analysis of over 30 seconds (Andreassi, 2007, p. 415).
HR increases have been found to occur during stressful events (Melillo, Bracale, &
Pecchia, 2011), the performance of cognitive tasks (Allen, Obrist, Sherwood, & Crowell, 1987;
Szabo & Gauvin, 1992) and during experiences of shock, fear and anger (Andreassi 2007, p. 440).
An intuitive example of a cardiac response to a psychological event may be that of a ‘racing heart’ in adverse or anxiety-inducing situations.
Heart Rate Variability
Heart rate variability (HRV) is a measure of autonomic activity that describes variations in the HR through the measurement of the periods between beats over time (Task Force of the
European Society of Cardiology and the North American Society of Pacing and
Electrophysiology, 1996). As with HR analysis, the distance between R waves is measured; however, of interest in HRV is the variation in the beat-to-beat (or R-R) intervals. HRV is typically analysed in time or frequency domains (although other methods, such as geometric, do exist). The time domain method may include investigating the difference between the longest and shortest R-
R intervals, the difference between HR in separate conditions and differences in spontaneous HR response to stimuli (Task Force of the European Society of Cardiology and the North American
Society of Pacing and Electrophysiology, 1996). The frequency domain method identifies three
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main spectral components of the HR: very low frequency (VLF), low frequency (LF) and high frequency (HF). HF has been found to decrease under conditions that evoke time pressure, emotional strain and state anxiety (Nickel & Nachreiner, 2003; Jönsson, 2007) and is considered a direct measure of parasympathetic activation (Berntson et al., 1997); this is also true of HF peaks, which are believed to reflect parasympathetic nerve activity (Billman, 2013). The role of LF/HF ratio is contentious within psychophysiological literature, with research identifying it as an inaccurate measure of cardiac sympatho-vagal balance (Billman, 2013).
Decreases in HRV indicate increased mental and physical stress (Schubert, Lambertz,
Nelesen, Bardwell, Choi & Dimsdale, 2009). For example, Melillo et al. (2011) compared the
HRV of university students taken during examination (a stressor) and immediately after returning from university holidays (the non-stressor, or control session); a significant HRV decrease was found in the stressor/examination session. Associations between decreases in the HF component of HRV and increased mental workload and attentional focus have also been found (Cinaz et al.,
2013; Hjortskov, N., 2004).
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2.8.7.4 Electroencephalography
Figure 11. EEG traces during various mental states. Reprinted from Psychophysiology: Human Behavior & Physiological Response (Andreassi, 2007, p. 68).
EEG measures the electrical activity of the cerebral cortex as recorded from the scalp
(Stern et al., 2001, p. 79), allowing for insights into cortical activity that occurs during experiences of attention, perception, sleep, sensation and emotional function (Andreassi, 2007, p. 115;
Davidson, Jackson, & Larson, 2000, p. 27). This electrical activity is generated by postsynaptic potentials of cortical nerve cells that originate in the cerebral cortex (Sanei & Chambers, 2008).
EEG activity is processed via two parameters: amplitude (the size of the signal) and frequency
(how fast the signal cycles), allowing for the detection of patterns, or ‘bands’, that emerge in cognitive activity (Stern et al., 2001, p. 80). The increases and decreases in power of the bands represent differing neurobehavioural states, summarised in Table 1, are as follows:
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Delta Activity (0.5–4 Hz)
Delta activity is a low-frequency band that only emerges in adults during deep, or slow wave, sleep (see the lowermost trace ‘Deep Sleep’ in Figure 11). The analysis of this band is generally exclusive to sleep research.
Theta Activity (4–8 Hz)
Theta activity has been found to occur in both states of drowsiness, rapid eye movement
(REM) sleep, problem-solving and attentional focus (Stern et al., 2001, p. 81). Presence of theta activity has also been found in situations of inhibited response, in which a response to stimuli is suppressed (Kirmizi-Alan, Bayraktaroglu, Gurvit, Keskin, Emre & Demiralp, 2006).
Alpha Activity (8–12 Hz)
Alpha activity occurs in awake, relaxing individuals, particularly when their eyes are closed. The rhythms that emerge during this are called the ‘alpha rhythms’, and are associated with relaxation and the relative lack of cognitive processes (Stern et al., 2001, p. 80). This state is known as ‘relaxed wakefulness’ (Davidson et al., 2000, p. 31), and primarily originates in the posterior and side regions of the head. Interrupting this state by, for example, asking an individual to undertake a complex cognitive task or introducing a stressor, is known as ‘alpha blocking’
(Stern et al., p. 80). An alpha wave is demonstrated in the second wave from the top (‘Relaxed’) in Figure 11.
Beta Activity (18–30 Hz)
Beta activity occurs during states of alertness, and is most common when an individual is engaged in acts of mental or physical activity (Andreassi, 2007, p. 69). It is commonly associated with states of active or anxious concentration (Baumeister, Barthel, Geiss & Weiss, 2008), and indicates activation associated with cognitive task demands (Fernandez, Harmony, & Rodriguez,
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1995; Ray & Cole, 1985). A typical beta frequency trace is found in the topmost trace (‘Excited’) of Figure 11.
Gamma Activity (32+ Hz)
Gamma activity has been correlated with exposure to sensory stimuli such as flashing lights or auditory clicking (Andreassi, 2007, p. 69), as well as the cognitive processing associated with the distinction between non-important and important stimuli (for example, trying to find a hidden object in a picture) (Stern et al., 2001, p. 81). However, some studies have suggested that gamma activity is a by-product of electromyographic interference, such as ocular movement, and not indicative of cognitive processing (Whitham et al., 2007; Whitham et al., 2008).
Table 1. EEG frequency bands and associated states.
Band Frequency Associated States Delta 0.5 – 4)( Hz Deep sleep
Theta 4 – 8 Hz Drowsiness; REM; problem-solving; attentional focus; inhibited response Alpha 8 – 12 Hz Relaxed wakefulness; closed eyes
Beta 18-30 Hz Active or anxious concentration; task demands
Gamma 32+ Hz Exposure to sensory stimuli; cognitive processing of visual stimuli
EEG activity is obtained through both a multitude of single electrodes separately attached to the scalp, or—more commonly—through a cap, headset or net system that contains all electrodes (Davidson, Jackson, & Larson, 2000, p. 35). EEG headsets that feature as few as a single electrode or channel are available, such as the NeuroSky MindWave headset (NeuroSky,
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2015), but greater coverage is provided in 14-, 16- and 32-channel arrays, as well as high-density
256-channel systems (EMOTIV, 2016; Davidson, Jackson, & Larson, 2000, p. 35).
Electrode sites are chosen in accordance with the 10‒20 system (see Figure 12), which allows for standardised placement of electrodes across the scalp. It is accepted research practice to refer to the data collected by individual electrodes by their site in the 10‒20 system, which identifies the region the electrical activity was collected from. The numbers on the 10‒20 system indicate hemisphere (odd numbers = left, even numbers = right, z = midline), whereas the letters indicate general cortical zone (O = occipital, P = parietal, C = central, T = temporal, F = frontal).
For example, activity obtained from the O2 site would refer to activity from the right hemisphere of the occipital lobe. As both general cognitive processing and EEG bands originate in various regions of the brain, the consistency of record-keeping in accordance with the 10‒20 system is imperative for comparing findings across laboratories and studies (Davidson, Jackson, & Larson,
2000, pp. 32‒33; Andreassi, 2007, pp. 72–73).
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Figure 12. International 10‒20 System. Reprinted from Psychophysiology: Human Behavior & Physiological Response (Andreassi, 2007, p. 72).
2.8.8 Limitations
Psychophysiological assessment contributes several advantages to the process of evaluating experiences and states: it allows for a covert, direct evaluation of emotional signals in response to an event or experience (Nacke, 2013), facilitates real-time response analysis and provides objective data that is unlikely to be influenced by intentional obfuscation or dishonesty. However, there are limitations associated with the method—in particular, the complexities of collection, analysis and interpretation.
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The complex domain relationships of psychological states and physiological response means that interpreting a physiological response as indicative of a specific psychological state is often limited; this is further bolstered by the presence of directional fractionation.
Psychophysiological analysis—especially tonic analysis—thus benefits from the inclusion of additional measures for comparison.
Set-up, data treatment and analysis of psychophysiological data also represent notable time investments. Such investments for these processes within the current research program are detailed in section 6.4.1. Financial costs signify an additional barrier to entry: initial purchase of clinical psychophysiological equipment is a costly endeavour, with the resupply of relevant consumables
(such as electrode gel or disposable electrodes) creating additional ongoing costs throughout data collection.
Finally, while psychophysiological assessment does allow for the covert recording of physiological response (in that participants do not need to answer directly), placing electrodes on surfaces of the body limits the naturalisation of the experiment. Furthermore, many instruments— such as EEG caps—may be uncomfortable for the participant.
2.9 PSYCHOPHYSIOLOGY OF THE PLAYER EXPERIENCE
Since 2004, researchers such as Mandryk (2004, 2006a, 2006b, 2007), Nacke (2008, 2009,
2010a, 2010b, 2010c) and Ravaja (2006a, 2006b, 2008) have published multiple papers exploring the relationship between psychophysiology and player experience. These papers have largely successfully shown the value of psychophysiological measures as a means of monitoring player experience.
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Psychophysiology has also experienced growing popularity within the games industry.
Newell (2008), co-founder and managing director of the video game development company Valve
Corporation, has stated that ‘we’re really interested in … testing biometrics on player state’.
Ambinder (2011) reveals Valve’s adoption of psychophysiology not only in terms of evaluating the player experience, but in creating games that respond dynamically to the player—such as a dynamic artificial intelligence (AI) director for one of Valve’s flagship titles, Left 4 Dead 2 (2009).
Collaboration between industry and academic groups has also been present in video game developer and publisher PopCap Games’ funding of Russoniello’s (2009a, 2009b) psychophysiological studies of casual game effect on mood. The growing prominence of psychophysiology within both research and industry necessitates an understanding of the more popular psychophysiological measures’ capabilities and usage in the player experience context.
2.9.1 Validating the Psychophysiological Method in Games
A number of studies exploring the applicability of physiological measures and methodologies in the player experience space, as well as the validation of physiological results in the context of video games, have been undertaken; for a review, see Kivikangas et al. (2011).
These studies help situate the efficacy of psychophysiology within games research and evaluation.
Mandryk, Inkpen and Calvert (2006b), in the first of two experiments, explored whether there were associations between a player’s physiological state, events occurring during the entertainment experience and the subjective reported experience. EDA, ECG and EMG (ZM and
CS) were used in concert with interviews, questionnaires (on a custom Likert scale measuring for challenge, frustration, boredom and fun) and video analysis. Mandryk et al. later discarded video analysis due to the substantial time commitment required. Eight male university students, aged
20—26, played three versions of hockey game NHL 2003 (Electronic Arts, 2002): easy, medium
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and difficult. No main effects of difficulty level were found on any of the physiological measures, although some effects of player expertise were found: self-identified expert players had higher respiration rates than the semi-experienced and novel players, self-identified novice players had higher HR in the easy condition, and self-identified semi-experienced players had higher HR in the difficult condition. The researchers later determined that the participants were responding inconsistently to experimental manipulations (no differences in perceived difficulty were found between the medium and difficult levels). Additionally, the researchers discovered that the process of interviewing had lasting effects on psychophysiological response. In Figure 13, the lighter grey columns represent the interviews; the effects of the interviews on arousal have affected the preceding darker columns, which represent gameplay. The researchers suggest that various interview issues (the gender of, or unfamiliarity with, the interviewer, personal space issues and nerves) may have influenced the psychophysiological data.
In a following experiment, Mandryk and colleagues relegated the interviews to online questionnaires so as to minimise human interaction effects on psychophysiological data. Vicente’s
(as cited in Mandryk et al., 2007) recommendation for the collection of baselines through the experiment as a regulation for such events was also incorporated. As the primary aim of the study was to explore the use of psychophysiology in player experience evaluation, the researchers also introduced a more potentially effective manipulation to the experiment design by incorporating a multiplayer game mode (play against a co-located player, and play against AI). ECG, HR and
EMG (ZM and CS) data was collected from 10 male participants aged 19—23. Tonic EDA was significantly higher in the co-located play condition than in the AI condition, as was mean EMG
(the authors do not differentiate between sites). Physiological and subjective data was also found to correspond: those who found the AI condition more challenging had higher tonic EMG in the
AI condition than in the co-located condition. EDA also proved useful for phasic analysis, showing
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greater EDA in goals scored against the co-located player than those scored against AI. The alterations to the experiment design thus proved helpful, and the researchers’ findings supported the hypothesis that physiological results will correspond to subjective reported experience. It is evident that a considered mixed-methods approach and a careful methodology can enable richer insight into the player experience.
In a study designed to explore the utility of EEG for evaluating the player experience,
Nacke (2010) implemented EEG, the Game Experience Questionnaire (GEQ) and the MEC
Figure 13. EDA during play and interviews, wherein the light grey columns represent interviews. Reproduced from ‘Using psychophysiological techniques to measure user experience with entertainment technologies’ by R. L. Mandryk, K. M. Inkpen, and T. W. Calvert, 2006, Behaviour and Information Technology, 25(2) p.150.
Spatial Presence Questionnaire (SPQ) in an experiment investigating affective gameplay interaction through a comparison of a PlayStation 2 (PS2) controller and a movement-based
WiiMote. This research was successful in discovering correlations between EEG and GEQ/SPQ, suggesting that EEG is a valuable tool for understanding the neurology of the player experience.
Thirty-six university students (7 female) between the ages of 18—41 played first-person shooter
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horror game Resident Evil 4 (Capcom, 2005) in both a PS2 and WiiMote condition. Nacke found a number of significant EEG band responses corresponding with interactions between participant expertise and experiment condition. Additionally, gender was found to have moderating effects on alpha, beta and gamma activity, due to a tendency for stronger psychophysiological response in women (Cacioppo et al., 2000). Correlations were also found between EEG bands and
SPQ/GEQ, such as a positive correlation between alpha power and reported tension ratings.
In an investigation of measuring emotional valence in video game play, Hazlett (2006) employed EMG ZM and EMG CS to measure psychophysiological responses to positive and negative video game events. Thirteen male participants, aged 9—15 years old, played Xbox car racing game Juiced (THQ, 2005) versus AI opponents. Hazlett found greater EMG ZM (the
‘smiling muscle’) activity in events identified in video review as positive (corroborated by discussion with expert players), and greater EMG CS (the ‘frowning muscle’) activity in events likewise identified as negative. These findings helped to situate EMG as an effective tool for the measurement of positive and negative player experiences.
Finally, Ravaja, Saari, Salminen, Laarni and Kallinen (2006a) used only psychophysiological (EMG, EDA and ECG) measures in conjunction with events in games to determine phasic emotional reactions. Thirty-six university students (11 female) aged 20—30 years old participated in the study. Psychophysiological feedback was compared with events in game, so as to confirm the hypotheses that unequivocally rewarding events—such as picking up a banana within Sega’s Super Monkey Ball 2 (2002)—would elicit positively valenced arousal, as indexed by increased ZM and OO EMG activity, increased EDA and increased HR. The researchers were successful in proving these hypotheses, situating physiological measures as an appropriate measure for player experience analysis. Ravaja and colleagues later employed
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psychophysiological analysis in a study investigating phasic emotional responses to violent video game events (Ravaja et al., 2008), as discussed in section 2.9.2.1.
These studies offer important implications for future research projects in the field of psychophysiological evaluation. The success of the studies conducted by Mandryk et al. (2006b),
Nacke (2010b) and Ravaja et al. (2006a, 2008) point to the potential of a mixed-methods approach—subjective and psychophysiological—in evaluating the game player experience. They also help to identify methodological flaws that can erroneously influence psychophysiological data, as in the case of participant‒researcher interaction.
2.9.2 Game Effects
2.9.2.1 Violence A central focus of psychophysiological game evaluation is distinguishing the effects different types of games or play experiences may have on players (Kivikangas et al., 2011). Of particular interest is the effect of violent game content, both physiologically and psychologically.
Kivikangas et al. (2011) suggest that existing psychophysiological research in the space is contradictory and often flawed methodologically, as a result of a tendency to compare subjective and physiological responses to different games or different genres (e.g. one violent, one non- violent). This lack of direct comparison introduces the risk of uncontrolled variables that influence data.
One example of this is evident in studies undertaken by Fleming and Rickwood (2001) and Ballard and West (1996), as reviewed in Kivikangas et al.’s (2011) meta-analysis of psychophysiological games research. Fleming and Rickwood employed HR analysis, as well as subjective measures of mood and arousal, to investigate the relationship between children’s mood and violent video games. Seventy-one children (35 female) aged 8—12 participated in two non-
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violent game conditions and one violent game condition. Both physiological (HR) and subjective arousal were found to increase significantly after exposure to the violent game condition.
However, it is important to contextualise these findings: the two non-violent game conditions were a pen-and-paper maze game and a geometric puzzle video game, and the violent game condition was an action-adventure combat-based video game Herc’s Adventures (LucasArts, 1997), rated
G8+ (no parental guidance recommended for children aged eight or older). It is impossible to determine whether the increased physiological response was to violent content, or other variables that were not controlled for in the conditions—for example, graphical differences, story (present in the violent game, but not the non-violent), audio differences and so forth. Furthermore, as
Herc’s Adventures features heavily stylised and cartoon-like graphics, the effect of the violence may not be comparable with that of the violent games employed in other literature discussed here.
Similarly, Ballard and West (1996) compared HR and BP for 30 male undergraduates across three conditions: one non-violent (billiards) and two violent (two versions of Mortal Kombat II
[Midway Games, 1993], one featuring less violent graphical content than the other). Again, higher
HR was found in the violent game conditions (Mortal Kombat II) than in the non-violent game condition (billiards). The differences between billiards and video games are such that a direct physiological comparison is problematic; as discussed in section 2.8.6.5, the many-to-one domain relationship suggests that a single physiological response may be indicative of many psychological events. More variables are controlled for in the direct comparison of the less and more graphically violent Mortal Kombat II versions, in which participants experienced greater BP reactivity in the
‘more violent’ version.
Gentile, Bender & Anderson (2017) investigated the effects of violent content in video games on physiological arousal, as measured by ECG and salivary cortisol, in 136 children (67 female)
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aged 8—12 years old. As with Ballard and West (1996) and Fleming and Rickwood (2001),
Gentile et al. (2017) compared psychophysiological response to violent and non-violent games
Spiderman [Activision, 2002] and Finding Nemo [THQ, 2003]. Both games were rated as ‘equally exciting’ by participants. Results revealed significantly increased cardiovascular arousal and increased cortisol in the violent game than the non-violent game, and are suggested as indicative of a fight-or-flight response in the participants. This methodology is strengthened by ensuring that each game experience was considered ‘equally exciting’ by the participants, thus controlling somewhat for gameplay variations as an explanation for the pattern of results; but, again, the research is limited in the comparison of two notably dissimilar play experiences.
Kivikangas et al. (2011) highlight research undertaken by Weber, Behr, Tamborini,
Ritterfield and Mathiak (2009) as notable in addressing these variations through the direct comparison of events—a phasic approach—within a single 50-minute first-person shooter video game (Tactical Ops: Assault on Terror, Kamehan Studios, 1999) session across 13 experienced male FPS players (aged 18—26) using EDA and HR measures. These events included situations such as ‘Round begins’, ‘Runs out of ammo during combat’ and ‘Killed opponent’. While EDA results did not reach significance, HR showed significant increases in response to certain events.
The events with the highest HR included ‘Player begins round’ and ‘Player ran out of ammunition under combat’. The authors speculate that the former finding suggests participant uncertainty as to their future performance and upcoming events, with the latter suggesting increased HR response to imminent perceived danger. The former finding is particularly interesting for psychophysiological methodologies within the video games space, as researchers may expect— and should account for—arousal increase at the beginning of a game condition, regardless of the intended game experience. Although the rigour of Weber et al.’s research benefits from the direct evaluation of participant experiences within the same game condition, it should be noted that the
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researchers allowed for the participants to freely select from 15 different maps. Weber et al. note that the maps were comparable in size and player mission, and that the selection of maps allows for a more naturalised player experience. Despite this, it is possible that differing map experiences influenced psychophysiological response between participants.
In Ravaja et al.’s (2008) phasic analysis of violent video game events, EDA, EMG OO, CS and EMG ZM activity was recorded from 36 (11 female) undergraduates, aged 20—30 years old, in response to the wounding and death of the player-character and enemy characters within the first-person shooter James Bond 007: Night Fire (Electronic Arts, 2002). In response to wounding and killing an opponent, Ravaja et al. found increased EDA and decreased ZM and OO activity; conversely, the wounding and death of the player-character evoked increased EDA, ZM and OO activity, with a decrease in CS activity. These results suggest an increase in high-arousal negative affect in response the killing and wounding of an opponent, and an increase in positive emotion in response to player death. While this was primarily a study of trait psychoticism among participants, with findings suggesting that those who rated highly on a psychoticism responded less negatively to enemy wounding or death, these results also have interesting implications for player experience—most notably that success in a game through defeating enemy units may be associated with increased negative valence.
In a separate investigation of the effect of both violence and difficulty, Kneer, Elson and
Knapp (2016) employed four modifications of the first-person shooter game Team Fortress 2
(Valve Corporation, 2007): a low-difficulty, high-violence condition; a low-difficulty, low- violence condition, a high-difficulty, high-violence condition; and a high-difficulty, low-violence condition, all within the same map. A sample of 90 (69 female) university students with a mean age of 24.47 was observed, with each participant playing one of the conditions. Kneer, Elson and
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Knapp employed IBI and EDA to evaluate psychophysiological arousal activation in each of the four conditions. The researchers did not find any significant effects on either IBI or EDA by difficulty, violence or their interaction, and suggest corroboration with ‘previous research indicating that violence in games does not substantially influence human behavior or experience’
(p. 142). The absence of differences in tonic psychophysiological response between these conditions further strengthens the possibility of game or genre difference, rather than the differences in violent content, as a primary contributor to disparate psychophysiological response in previous research methodologies.
2.9.2.2 Stress
Video games have previously been employed extensively as stressor stimuli in psychophysiological research. As such, video games have been found to consistently induce altered HR response and BP and increased cortisol levels in research exploring violence, challenge and stress (van der Vijgh, Beun, van Rood, & Werkhoven, 2015). Despite this, a meta-analysis by
Vijgh et al. (2015) of research employing video game stressor stimuli discovered specific and consistent moderating functions of both game and study characteristics, accounting for 43% of variance found in physiological response. Vijgh and colleagues state that ‘a digital game stressor does not act as a stressor by virtue of being a game, but rather derives its stressor function from its characteristics and the methodology in which it is used’ (p. 1080). The authors present this as evidence for the case for standardisation within psychophysiological and games research.
Additional psychophysiological research has also found evidence of video games as a form of catharsis or stress release. Russoniello, O’Brien and Parks (2009) employed EEG, HR and
HRV analysis, as well as subjective measures of mood, in an investigation of the effect of casual games on mood and stress across 69 participants (no demographic information was provided by
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the authors). Play of the casual video game Bejewelled II (PopCap Games, 2004) resulted in decreases in left frontal EEG alpha, leading Russoniello et al. to conclude increased mood, increased HRV and decreased HR, indicating decreased arousal contextualised by the subjective mood measure as a decrease in stress. Additionally, the researchers support further multi-modal use of psychophysiological and subjective measures.
2.9.3 Social Play
Psychophysiological evaluation of social play experiences, particularly in the comparison of human and AI teammates and enemies, has been an additional area of investigation in player experience evaluation. Mandryk and Inkpen (2004) represented one of the first of these efforts in their employment of EDA, EMG ZM and HR in assessing co-located competitive play against a friend and play against AI in the ice hockey video game NHL 2003 (EA Sports, 2002). For the friend condition, results revealed significantly greater EDA and EMG ZM activity, aligning with subjective experiences that revealed instances of increased fun, excitement and engagement. These findings establish increased EDA and EMG ZM as reflecting social experiences in play, and indicate a tendency for increased physiological activity in play against a friend than against a computer. However, the study was limited in sample size (10 male participants aged 19—23); additional exploration of these experiences is thus recommended.
Ravaja et al. (2006b) used ECG, EMG CS, OO and ZM across 99 (48 female) undergraduates aged 19—34 to evaluate competitive play against a human friend, human stranger and AI in the video games Super Monkey Ball Jr. (THQ, 2002) and Duke Nukem Advance (Torus
Games, 2002). Similar to Mandryk and Inkpen (2004), Ravaja et al. encountered higher levels of psychophysiological arousal when playing against human-controlled players, present in the form of shorter IBIs (or increased HR) and increased positively valenced emotional responses in the
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form of increased OO and ZM activity. Greater anticipated threat was also reported in play against humans, supported both by subjective measures and increased sympathetic arousal.
Finally, Lim and Reeves (2010) employed psychophysiological assessment to explore competitive and cooperative play experiences with human or AI counterparts, employing EDA,
SCR (skin conductance response) and HR within a sample of 34 (17 female) university students.
Lim and Reeves discovered that players experienced greater EDA, SCR and HR activity when playing with humans, irrespective of competitive or cooperative play. These results contribute to extant psychophysiological literature on social play experiences, supporting research establishing increased physiological arousal in play with humans than with AI (Mandryk & Inkpen, 2004;
Ravaja et al., 2006b).
An investigation into the psychophysiological effect of social play was undertaken throughout the author’s Honours year, and expanded upon during the first quarter of the PhD candidature (Johnson, Wyeth, Clark, & Watling, 2015). The psychophysiological effect of the player experience was explored by comparing play sessions with AI teammates and play sessions with co-located human teammates within the same map of the game Payday: The Heist (Overkill
Software, 2011). The study employed a mixed-methods approach to evaluation, using EEG in conjunction with selected subscales from the Player Experience of Needs Satisfaction scale
(PENS) and GEQ questionnaires. Seventy-two (80.6% male) participants, between the ages of
18—31 and self-describing as having at least some experience with video games, undertook the study.
In terms of brain activity, the study revealed that play with human teammates was associated with greater activity in the beta, theta and alpha power bands than play with AI teammates. The association of power bands with general mental states allowed for insight into the
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psychophysiological experience of the players. As greater activity in the beta band is typically associated with cognitive processes, decision-making, problem-solving and information processing (Lehmann et al., 2012; Nacke et al., 2011), the study indicated that participants were processing more information during play with humans. Similarly, theta has been linked to engagement and challenge in video game play (Salminen & Ravaja, 2007), potentially revealing that participants found play with human teammates more challenging or engaging than play with
AI teammates. Overall, the study suggests that play with human teammates involves greater cognitive activity than play with AI teammates.
Including questionnaires in the study bolstered understanding of EEG associations with subjective measures. It was found that greater activity in the beta band was associated with increased experience of presence when playing with AI teammates, implying that an increase in information processing is associated with feelings of presence. Associations were also discovered between autonomy and greater beta and gamma band activity in the AI condition; this is potentially attributable to increased freedom in decision-making and strategy. Johnson et al. (2015) propose that these results only emerge in the AI condition, and as a consequence, increased ‘mentalising’ in the human teammate condition, wherein concern for human teammate’s motivations and goals may have moderated the relationships between subjective and physiological results.
2.9.4 Immersion
While the concept of fun is difficult to quantify, concepts such as enjoyment, immersion, engagement and flow (see section 2.9.6) provide a helpful lens through which to evaluate the player experience. A primary concern of player experience research is to gain further insight and understanding of these experiences. Nacke and Lindley (2008, p. 1) emphasise the importance of investigating these concepts:
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Not only do these terms currently lack well-accepted common meanings, but also for game
designers, clear and testable definitions of constructs such as immersion and flow would be
invaluable, since these are considered to be the holy grail of digital game design.
In pursuit of this objective, Nacke and Lindley (2008) employed subjective (a GEQ and an SPQ) and psychophysiological measures—EDA and EMG (OO, ZM, CS)—in their evaluation of immersion and flow in first-person shooters. Only the findings on immersion are reported in this section (refer to the following section, 2.9.6, for flow). The researchers developed a boredom condition, immersion condition and flow condition modified from first-person shooter Half-Life
2 (2004). Data was collected from 25 male participants aged 19—38 years old. While the flow condition was successfully distinguished from the immersion and boredom conditions in both psychophysiological and GEQ data, there was not enough evidence to discriminate between experiences in the immersion and boredom conditions. Nacke and Lindley state that, while flow and boredom are intuitively understood, immersion is less so; it may be that the condition design was not able to accurately evoke a sense of immersion, or a distinct enough sense of immersion to separate it from experiences of flow or boredom. The boredom condition generated greater psychophysiological activity within EMG ZM, EMG OO and EDA; however, it is unclear whether this suggests decreased physiological reactivity during immersion, or whether this result is an artefact of the condition design. This establishes the difficulty of evaluating immersion, despite its ubiquity within player experience research.
Nacke, Gimshaw and Lindley (2010a) undertook an additional assessment of the player experience of first-person shooters through the psychophysiological and subjective evaluation of the effect of game sound (‘sonic user experience’). Participants were 36 (7 female) undergraduate students and university employees between the ages of 18—41. While a significant main effect of sound was discovered in the subjective analysis (finding that the inclusion of game sound led to a
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more positive player experience), no main or interaction effects of sound were found for either
EMG or EDA. Nacke et al. suggest that this may be because of the tonic approach used, in which reactions to sound were ‘averaged’ out; the authors identify tonic evaluation as suitable for some player experience analysis, but note that a phasic analysis may be more appropriate for evaluating sonic experiences in digital games.
2.9.5 Dynamic Difficulty Adjustment and Biofeedback
Most single-player video games allow for the adjustment of difficulty across several static options, typically in a spectrum ranging from ‘easy’ or ‘beginner’ through to ‘hard’ or ‘insane’; however, this lack of flexibility may lead to a mismatch between player skill and the challenges of the game (Huncke & Chapman, 2005). This issue is circumvented through the introduction of
DDA: reactive game systems that adapt dynamically to player performance, allowing for the maintenance of challenge‒skill balance (Hunicke & Chapman, 2005). This adjustment may be undertaken through such routes as the real-time manipulation of opposing AI units, quantities of resources available to the player and map layout to either increase or decrease the game difficulty based on player performance (Baldwin et al., 2013). The real-time nature of psychophysiological recording has situated psychophysiological analysis as uniquely suited for employment within
DDA contexts (Baldwin, 2017). An extension of this application is also found in use of biofeedback in video games, in which physiological response is used to directly and indirectly control game interaction (Nacke et al., 2011).
Mirza-Babei et al. (2013) investigated the use of psychophysiology in the creation of
‘biometric storyboards’, in which events in a game were mapped to players’ physiological responses and used to inform design decisions for further iterations of game development. Mirza et al. employed EDA, EMG CS, ZM and OO activity in their assessment of the play experience;
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once the data was collated, the researchers reviewed game video data with their 6 male participants to identify positive and negative experiences. These results informed further iteration of the game design. In later comparisons of both non-biometric and biometric storyboards, Mirza-Babei et al. found that the use of biometric storyboards resulted in a greater number of requested design iterations and increased confidence in these iterations. In an assessment of the final game designs across 24 experienced male PC gamers aged 19—27 years old, results revealed that the gameplay experiences informed by biometric storyboards were ‘significantly better’ as assessed by subjective measures. Mirza-Babei et al. suggest that these results highlight potential benefits of psychophysiological analysis in games user testing and development.
In a psychophysiological evaluation of adaptive game conditions in Tetris using both EDA and HR, with a sample of 18 (4 female) university students aged 19—31, Wu and Lin (2011) found that EDA signals were able to quickly and robustly adjust player stress changes. This was revealed through increased EDA in response to challenging or frustrating tasks. Furthermore, Wu and Lin suggest that the combination of EDA and HR may be used to identify a ‘negative stress’, or ‘distress’, threshold, which may be useful for DDA games to indicate the reduction of challenging or frustrating events. The authors add that the same interpretation may be made of decreased EDA, which could indicate boredom and thus promote the inclusion of challenging or frustrating events.
Nacke et al. investigated the use of ECG, EDA, EMG, respiration, temperature, and gaze as a method for both indirect and direct control of player experiences and interactions (Nacke et al., 2011). Ten participants (3 female) between the ages of 21—40 years old played two game conditions, in which one game condition featured direct physiological control (e.g., increased run speed through flexing leg muscle) and the other indirect physiological control (e.g., increasing
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game speed in response to HR). Results revealed a preference for direct physiological control, with authors suggesting the use of indirect physiological input as a ‘dramatic device’ for alterations of the game world.
Finally, Negini et al. (2014) explored the use of affective states—as measured by EDA— to control in-game difficulty, such that decreases in physiological arousal would prompt increased game difficulty and vice versa. The researchers examined this across three conditions, in which game difficulty was modulated through the real-time modification of the player-character, enemy
NPCS, or game environment; a control condition, sans affective adaption, was also included. 16 university students (15 male) between 18—32 years old participated in the study. Negini et al. found that real-time adaption of game difficulty increases physiological player arousal, treated by the authors as a measure of excitement. The authors also found reduced player enjoyment in the condition in which enemy NPCs were adapted, and point to the moderation of the player-character or environment as preferred routes for maintaining or ensuring positive play experiences.
2.9.6 Flow and Challenge
Despite the prominence of flow theory within the sociology and psychology fields, the construct has rarely been examined from a psychophysiological perspective (Peifer, 2012).
Traditionally, flow has been evaluated with subjective measures such as questionnaires and interviews after the experience of flow has ended. Peifer (2012, p. 139) states,
The important conflict here is that as soon as participants are asked for their experience,
they enter into self-reflection and leave the flow state … psychophysiology can provide
physiological flow indicators that are assessed during the activity without interrupting the
participant.
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It is evident that the psychophysiological investigation of flow allows for a deeper understanding of the flow experience, particularly as it grants real-time insight into the experience.
Like psychophysiological evaluation of flow outside the field of video games, psychophysiological evaluation of flow experienced during gameplay is rare. As discussed in section 2.9.4, Nacke and Lindley (2008) used EMG OO, CS and ZM, as well as EDA, gathered from 25 male participants aged 19—38 years old, in their evaluation of flow within a first-person shooter environment. The boredom condition was designed to induce boredom, largely by ensuring that player skill outstripped the challenge of the level; this was achieved by providing a large quantity of health and ammunition supplies, reducing opponent strength and diminishing the number of enemies. By contrast, the flow condition encouraged the flow state through gradual increase in game difficulty, interesting combat mechanics and ‘cool down’ spots to diminish the risk of overwhelming players. Nacke and Lindley found a correlation between facial muscles indicating positive valence and the flow condition; similarly, EDA response showed greater activation in the flow condition than the boredom condition. It should be noted that Nacke and
Lindley’s boredom condition was intended to detract from immersion in the game; it achieved this through repeating textures, dampened sounds a linear level. It is important to understand that this study compared flow to boredom (assumed by Nacke and Lindley to be a combination challenge‒ skill imbalance and low immersion), as opposed to a pure comparison of flow and no/low flow.
A study of 32 male participants (aged 17—32 years old) by Kivikangas (2006) does not support Nacke and Lindley’s (2008) psychophysiological findings, finding no relationship with flow and facial muscles indicating positive valence. Kivikangas also detected no relationships between physiological arousal, as measured by EDA, and flow. The author suggests that this may be because of the length of the play sessions in their experiment design (approximately 40
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minutes); there is a possibility that averaging over long periods of time may have reduced the associations between flow and psychophysiological response due to the potential for habituation, as discussed in section 2.8.6.1.
Keller, Bless, Blomann and Kleinbohl (2011) revealed that reduced HRV, indicative of enhanced mental workload, was associated with challenge‒skill balance. Increased HRV was associated with boredom assessed within the study, reflecting lower mental load. Similar to Nacke and Lindley, Keller at al. created a boredom condition and a challenge‒skill balance condition in which balance was dynamically maintained (referred to as a ‘fit’ condition). A third condition, in which the challenges of the game overwhelmed the skill of the player, was also created to measure stress. These conditions were tested across eight (four female) university students; further exploration of challenge‒skill balance across a larger sample would allow for greater interpretability of results. The game used in the experiment—a version of the game show Who
Wants to be a Millionaire—is arguably better categorised as a digital quiz. The relevance of this study to the broader digital games literature may thus be limited. Despite this, Keller et al. suggest that ‘flow experiences represent a distinct state that can be identified not only with self-report data but also on physiological measures’ (2011, p. 852).
Keller et al. (2011) also employed the same experimental approach discussed in section
2.3.1 to evaluate salivary cortisol levels across three difficulty levels in Tetris, featuring a sample of 61 male university students (Keller and Bless, 2008). Salivary cortisol is considered a robust indicator of physiological response to stressful stimuli, since increased synthesis of cortisol occurs in response to psychological and physical stress (Kirschbaum & Hellhammer, 2000). Results revealed relatively high levels of salivary cortisol in both the fit and overload conditions, with no significant differences revealed between either; Keller et al. thus propose that flow experiences
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may indicate increase physiological stress. They suggest that this may be a form of ‘positive stress’; however, an elevated mood state was not corroborated by subjective measures employed within the study.
Harmat et al. (2015) undertook a psychophysiological assessment of flow using similar game conditions developed in Tetris, as in research by Keller et al. (2008; 2011). Featuring a sample of 77 (40 female) participants (mean age of 27.8), Harmat et al. employed functional near- infrared spectroscopy (fNIRS; a measure of oxygenation in the prefrontal cortex), ECG (HR and
HRV) and respiratory analysis to assess the physiological correlates of the flow experience across easy, optimal and difficult conditions modulated by challenge‒skill balance and imbalance.
Results revealed higher flow experiences were associated with larger respiratory depth, which
Harmat et al. suggest indicates an association between increased flow and more relaxed physiological states; furthermore, the authors propose an association of reduced LF and increased flow to suggest deeper immersion in a flow-like state, as supported by previous research finding decreased LF in meditation (Krygier et al., 2013). No relationships were discovered between prefrontal cortex oxygenation, HR or the HF component of HRV.
Drachen, Nacke, Yannakakis and Pedersen (2010) used HR and EDA in their assessment of physiological correlates of player experiences from 16 participants—no demographic information was provided by the authors—in three first-person shooter games. In their approach,
Drachen et al. explored several psychological constructs of play, including flow and challenge, within a single subjective scale. HR was found to correlate negatively with flow and challenge, with a low HR indicating increased flow and challenge experiences; however, no significant correlation was found between EDA and flow, or EDA and challenge. Drachen et al. propose the
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absence of results for EDA and flow/challenge as a function of imprecise wording for the scale used; this emphasises the difficulties of determining challenge in player experience evaluation.
2.9.7 Gaps Identified in Player Experience Literature
Overall, the physiological response to flow and challenge within video games is not yet well understood. While some literature in the field generally points to physiological response, indicating mental stress as associated with flow and challenge‒skill balance (Drachen et al., 2010;
Keller et al., 2011), results from other research does not support this (Kivikangas, 2006).
Understanding the role of positive valence in the assessment of flow and challenge is also limited, with contradictory results emerging from player experience literature (Kivikangas, 2006; Nacke
& Lindley, 2008). Additionally, with occasional exceptions, psychophysiological assessment of the player experience is often limited by small sample sizes and a lack of uniformity across methodologies and experiment designs (see Table 2 for a summary). As such, while psychophysiological measurement represents a keen area of interest for player experience research, both the employment of psychophysiological measures and the results reported are often inconsistent and somewhat limited by methodology and sample size. This points to an obvious need in psychophysiological player experience literature for cohesive research that addresses these limitations through implementation: one that aligns with recommended psychophysiological practice, employs a large sample size, and includes the use of most commonly used psychophysiological measures to investigate a construct or constructs of the player experience.
A unified psychophysiological analysis of the concept of flow and challenge would benefit games literature by allowing for a richer understanding of flow (enabling insight into the moment of flow), which would improve current understanding of the player experience. A
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successful psychophysiological analysis would also introduce psychophysiology as a viable and proven method for evaluating flow in future research and play-testing settings.
Table 2. Overview of research discussed in Chapter 2 Authors Year Measures N Area of Investigation Ballard & West 1996 ECG (HR), BP 30 (M) Violent v. non-violent games, hostility Drachen et al. 2010 HR, EDA 16 Flow, challenge, enjoyment, tension Fleming & 2001 ECG (HR) 71 Violent v. non-violent games, Rickwood aggression Gentile et al. 2017 ECG (HR, arterial 136 Violent v. non-violent games pressure), Cortisol Harmat et al. 2015 fNIRS, ECG, resp. 77 Flow Hazlett 2006 EMG (CS, ZM) 13 (M) Valence during events Johnson et al. 2015 EEG 72 Play with AI and humans Keller & Bless 2011 Salivary cortisol 61 (M) Flow Keller et al. 2008 ECG (HRV) 8 Challenge‒skill balance Kivikangas 2006 EMG, EDA 32 (M) Flow Kneer et al. 2015 EDA, ECG (IBI) 90 Violence, difficulty, affect Lim & Reeves 2010 EDA, SCR, ECG 34 Competition and cooperation (HR) with humans and AI Mandryk et al. 2006 EDA, ECG, EMG 8 (M) Difficulty Mandryk et al. 2004 EDA, ECG, EMG 10 (M) Difficulty, play with AI and humans Mirza-Babei et al. 2013 EDA, EMG 6 (M) Biometric storyboards Nacke & Lindley 2008 EMG, EDA 25 (M) Immersion, flow, boredom Nacke 2010 EEG 36 Controllers Nacke et al. 2010 EDA, EMG 36 Sonic experience Nacke et al. 2011 EDA, ECG, EMG, 10 Direct and indirect resp., temp., gaze physiological control Negini et al. 2014 EDA 16 Physiologically-adapted DDA Ravaja et al. 2006 EMG, EDA, ECG 36 Phasic responses to video game events Ravaja et al. 2008 EMG, EDA 36 Phasic responses to violent video game events Russoniello et al. 2009 EEG, ECG (HRV) 69 Stress, mood Weber et al. 2009 EDA, ECG (HR) 13 (M) Phasic responses in violent games Wu & Lin 2011 EDA, HR 18 Assessment of DDA M = male participants only
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3 RESEARCH DESIGN AND METHODOLOGIES
3.1 RESEARCH STRUCTURE AND SCOPE
As explored in the literature review, psychophysiological evaluation offers rich insight into the player experience. However, the complexities and considerable temporal costs associated with the psychophysiological method have directly influenced its employment within games research and literature; despite the advantages of this approach, psychophysiological games studies have typically featured small sample sizes, or restricted psychophysiological investigation to few psychophysiological measures (see section 2.9.7). Disparities in methodologies have also been identified as a potential source for discrepancies in findings, creating demand for a single unified approach to the psychophysiological analysis of the play experience (Bernhaupt et al., 2008).
To this end, a rigorous program of research exploring the psychophysiological effect of play was undertaken. The aim of this research is driven by gaps observed within existing psychophysiological player experience literature, with the intention of strengthening and contributing to the contemporary understanding of the psychophysiological experience of play.
Research Aim
1. To further clarify existing contributions to literature by expanding understanding of the
psychophysiological experience of play, and the value of psychophysiological
measures as a means of assessing the player experience.
To achieve this aim, the stringent application of psychophysiological method—as informed by practice in the larger (beyond player experience) psychophysiological research space—was essential. In concert with the existing psychophysiological player experience literature, the most
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commonly used measures are deployed in this program of research. Furthermore, to ensure relevance to player experience research, an approach employing both subjective and objective measures was undertaken; this allows for an informed physiological exploration of self-reported psychological constructs (as assessed by validated scales) already investigated in the context of player experience. These constructs include phenomena such as flow, affect and needs satisfaction, established in Chapter 3.
A set of criteria for the study methodology was established to enable a robust exploration of the research aim and address gaps identified in the literature. The final methodology was informed by the following considerations:
a) feature a large sample size composed of males and females
b) employ multiple measures of physiological assessment that may be feasibly deployed
in games research and play-testing
c) consider previous research in both psychophysiological and play experience literature,
and develop a methodology congruent with practice in the psychophysiology field
d) employ a video game artefact that represents the contemporary standard of commercial
games in order to maximise generalisability
e) explore prominent psychological constructs of video game play, as identified in the
literature.
The approach chosen was a large-scale single study, featuring both psychophysiological and self-report measures and informed by a smaller scale pilot study to ensure successful experiment and condition design. The final study would also be closely iterated throughout an extensive design stage, as shaped by the pilot study aim, methodological considerations and outcomes. This also allowed for the psychophysiological evaluation of multiple subjective player
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experience constructs within the same framework, ensuring a consistent experience as necessitated by the complex domain relationships in psychophysiological analysis (see section 2.8.6.5). Of additional importance was the evaluation of a large sample size, established as a gap within the existing literature.
A distinct element of this research program was the study format. In lieu of two to three separate studies with smaller sample sizes of approximately 30–40, commensurate with existing literature, the decision was made to employ a single large-scale study featuring a greater sample size (approximately 90) and both psychophysiological and subjective measures. This would both directly address the dearth of large-scale psychophysiological studies in player experience, and allow for greater statistical power in analysis.
A key risk of this large-scale approach is the potential for incongruent experiment design to impede, eliminate or otherwise influence both the psychophysiological and subjective responses. This risk is minimised by an iterative design process throughout the development of both experiment methodology and software. An additional hazard lay in the practicality of the large sample size; previously, studies have been necessarily limited by the time costs associated with psychophysiological deployment and data analysis. This is best combatted by including a robust recruitment process and allotting adequate time to both data collection and analysis. The strengths and limitations of the methodology, as well as the processes and iterations undertaken for improving or ensuring design suitability and appropriateness, are further detailed in sections
6.4–6.5.
The expectation is that this research will expand current understanding of the psychophysiology of video game play, and address gaps identified within existing literature in the space. An additional expected contribution of this work is to provide insight into the logistics
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associated with contemporaneously employing multiple psychophysiological measures in player experience analysis, with direct implications for using psychophysiology in player experience research and commercial settings.
3.2 RESEARCH STAGES
To satisfy the aim detailed in section 3.1, and further contribute novel discussion to both games and psychophysiological literature, a multi-faceted research question was first established to direct the design of a single large-scale investigative study:
Research Questions
1. How effectively can psychophysiological measures be used to evaluate the player
experience?
a. What are the differences in psychophysiological response between optimal and
sub-optimal play experiences?
b. Which psychophysiological measures, or combination of psychophysiological
measures, most reliably predict specific components of the player experience
as assessed by subjective measures?
Guided by these questions, the program of research adopted a four-stage process of design, iteration, data collection and analysis (see Figure 14). To satisfy the requirements of RQ1a, both optimal and sub-optimal play experiences were developed as video game artefacts within stages 1 and 2.
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•determine a distinct study design for the comparison of physiological responses •design and develop a video game artefact representative of a contemporary commercial game STAGE 1 •identify a viable physiological approach
•pilot game artefact to determine effectivity; iterate and refine upon the design •develop an experimental design congruent with psychophysiological measurement STAGE 2 •develop software that allows for partial automation
•data collection for final study STAGE 3
•investigation of RQs 1a and 1b STAGE 4
Figure 14. Research stages.
3.2.1 Summary Stage 1—Development of Methodologies and Game Artefact
As one aim of the research program was to expand upon the psychophysiological
understanding of the player experience and its application to evaluation, it was imperative to
identify both the prominent psychological constructs for investigation and the most effective
approach for the psychophysiological examination of these constructs. Examining play experience
literature revealed flow as a source of considerable interest within games research, applicable to
psychophysiological investigation and evaluable within the study scope.
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To investigate the psychophysiological experience of this construct, it was determined that two conditions capable of (respectively) inducing and inhibiting these experiences would be beneficial. This would allow for the direct comparison of physiological response, facilitating a refined identification of the response source, or lack thereof. In the early stages of this program of research, a focus on flow (to the exclusion of other subjective player experience constructs) was considered, but subsequently identified as problematic during the piloting stage (see section 4.3).
It was also integral that the video game artefact chosen should represent contemporary commercial games and thus be generalisable across the existing play experience landscape. To this end, Valve Corporation’s popular first-person shooter Left 4 Dead 2 (2009) was selected and modified for experimentation to have two conditions: a sub-optimal condition in which skills significantly outstripped demands (the Boredom condition, expected to lead to low levels of flow), and one in which the game dynamically ensured challenge‒skill balance (the Balance condition, expected to lead to higher levels of flow).
Finally, a viable psychophysiological approach was identified based on deployability, temporal efficiency, ease of access and interpretation, and potential for adoption within play experience evaluation. The chosen approach consisted of EEG, EDA, ECG, EMG (ZM), EMG
(CS) and respiration; respiratory analysis was later removed from the methodology in Stage 2 due to the respiratory belt’s tendency to overlap the ECG electrode situated on the rib.
3.2.2 Summary Stage 2—Pilot Testing and Design Iteration
A third sub-optimal condition, in which the demands of the condition outstripped the skill of the player, was introduced to represent the spectrum of challenge‒skill balance and imbalance
(the Overload condition). The addition of this condition also represented a more nuanced approach to exploring optimal and sub-optimal play experience, as necessitated by RQ1a.
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The penultimate iteration of the video game artefact was pilot tested in an approximation of the final methodology, without psychophysiological measurement, to determine the effectiveness and reliability of the condition design. The pilot test surveyed participants on their experience of flow during the three play conditions, revealing, unexpectedly, no significant differences in flow between the Boredom and Balance conditions (see Chapter 4 for details).
These results informed the final iteration of the video game artefact design; possible explanations for these findings suggest either an error in the development of the game conditions, or possible limitations of the flow scale for the purposes of evaluating flow in a video game with low levels of challenge. Potentially engaging elements of the Boredom condition were thus removed to ensure a sub-optimal play experience. Furthermore, the scope of the research program was broadened to include evaluation of presence, autonomy, competence, enjoyment and affect as well as flow (in a reduced capacity); again, this allowed for a fuller definition of optimal and sub- optimal player experience, strengthening exploration of the aim and research questions. Some minor adjustments were made to the Balance condition to ensure a similar experience among all participants. The experiment procedure was also refined for final deployment.
Finally, sequencing software was developed to minimise interaction with participants, reduce experiment runtime, ensure accurate time lengths for all conditions and baselines, and allow a single researcher to facilitate the experiment. This software was deployed for data collection after extensive testing.
3.2.3 Summary Stage 3—Data Collection and Analysis
The final iteration of the study, including psychophysiological measures and the additional subjective measures, was deployed alongside the sequencing software. The final methodology is detailed in section 3.5 and Chapter 5. Data collection took place over a period of
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13 months (March 2015 to April 2016), resulting in a sample size of 90 male and females aged
17+. Data was intermittently cleaned and evaluated throughout collection to ensure the quality of the physiological data, identify potential trends and report preliminary results.
3.2.4 Summary Stage 4—Evaluation
Data was prepared and cleaned in accordance with the suggested methods for physiological data treatment, with movement and noise artefacts—as identified by both visual scanning and thresholds set within the analysis software—removed prior to analysis (for a full write-up of the time costs associated with this process, see section 6.4.1). A spectral analysis of two EEG sites, tonic mean analysis of EDA and EMG, and HR and HRV analysis of ECG was performed. Analysis included a multivariate analysis of variance (MANOVA), analysis of variance (ANOVA), correlations and regressions. This stage featured the investigation of RQ1a and 1b, and the research aim.
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3.3 STAGE 1—DEVELOPMENT OF METHODOLOGY AND ARTEFACT
Identification of a study design; selection of physiological approach; video game artefact design and development
3.3.1 Introduction
As discussed in section 3.1, the aim and research questions of this research program stipulate expanding upon existing research by evaluating psychophysiology in three contexts: evaluating psychophysiology as a predictor of the subjective experience; the differences in psychophysiological response between optimal and sub-optimal play experiences; and exploring the value of psychophysiological measures for assessing the player experience. RQ1a further seeks to explore what differences may emerge in the psychophysiological response to both optimal and sub-optimal play experiences. It was also established that the research should achieve this by identifying and resolving pertinent gaps in psychophysiological play experience literature.
To this end, it was established that the program of research should feature a large sample size, multiple physiological measures, relevant subjective measure(s) and a contemporary video game artefact, and be rooted in established psychophysiological method. The objectives of Stage
1 are as follows:
determine a distinct design for the comparison of physiological responses (optimal v. sub- optimal)
select relevant subjective constructs
design and develop a video game artefact representative of a contemporary commercial game
identify a viable physiological methodology.
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3.3.2 Theoretical Grounding for Study Design
Due to the nature of domain relationships in psychophysiological measurement, wherein—for example—one physiological response may indicate multiple psychological processes (see section 2.8.6.5), a comparison of physiological responses to differing stimuli was undertaken for the purposes of isolating potential relationships. Of priority to the program of research (as identified in the research questions) is an exploration of how physiological response may differ between exposure to optimal and sub-optimal play experiences, and whether physiological response may be used to predict subjective experience of play experience phenomena. It was intended that this would aid in providing a metric by which to evaluate the experience of video game play through the patterns identified in physiological responses.
To more accurately isolate these relationships from multiple influences, it was determined that the video game conditions should differ in a quantifiable way. Csikszentmihalyi’s (1990) theory of the optimal experience through flow was thus adapted for the program of research.
Despite the prominence of the flow theory within player experience research and evaluation (Cox,
Cairnes, Shah, & Caroll, 2012), extant psychophysiological studies of flow in video games have so far employed relatively limited sample sizes of 25 and eight (Nacke & Lindley, 2008; Keller &
Bless, 2008). A program of research that collated data from a larger sample size would not only have the potential benefit of exploring and substantiating the conclusions of previous research, but also contributing its own findings of additional relationships discovered through improved statistical power. Employing a greater number of physiological measures, some potentially novel, than those used in previous research would also provide the opportunity to expand current understanding of the psychophysiology of flow in the play experience.
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As in previous research, the current program of research employed multiple artefact conditions for the purposes of psychophysiological comparison and prediction. Nacke and Lindley
(2008) used three game conditions to induce immersion, flow and boredom in a first-person shooter video game; of relevance to this research are the modifications made to the flow and boredom conditions. In Nacke and Lindley’s research, flow was designed for by including challenges focused on ‘interesting combat mechanics’, gradual increase in difficulty and
‘cooldown spots’ (areas that allow players to rest and restock with sparse ammunition and health supplies). The boredom condition focused on reduced challenge (weaker enemies and high amounts of health and ammunition supplies) and a ‘boring’ play environment (repeating textures, linearity and a limited choice of weapons). Nacke and Lindley’s boredom condition was rated by participants as low on challenge, immersion and flow. Conversely, the flow condition scored highest on flow, challenge and tension. The flow condition also elicited greater physiological arousal and greater positive valence than the boredom condition.
Keller and Bless (2008) achieved flow and low-flow conditions through direct manipulation of challenge‒skill balance in a Tetris-type video game. Flow was designed for in a condition referred to as ‘adaptive’, in which task demands automatically adapted to player skill through dynamic response to performance metrics. Low-flow states were achieved via boredom and overload conditions. In the boredom condition, blocks fell at a very slow speed regardless of player ability. In the overload condition, blocks fell at a fast base speed, which would continually increase during play. Keller and Bless’ manipulation was successful in achieving greater experience of flow than the low-flow conditions, as indicated by participant reports of several core flow characteristics. Participants playing the adaptive condition experienced an altered perception of time, perceiving the adaptive condition to be shorter than the boredom and overload conditions.
Participants also reported greater enjoyment and involvement in the adaptive condition than in the
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boredom and overload conditions. This study identified challenge‒skill balance as a significant predictor of intrinsic motivation.
The condition designs undertaken in these studies informed the condition design within the current research program, representing a refinement and iteration of methodologies towards the psychophysiological evaluation of video game flow.
3.3.3 Identifying a Viable Physiological Approach
The selection of physiological measures for evaluation was predominately motivated by considering the availability and suitability of such measures in player experience research and application domains, such as commercial studio play-testing. It was also anticipated that identifying a robust, temporal and financially effective, and deployable psychophysiological method may encourage further adoption of psychophysiological evaluation within player experience research contexts. Determining the physiological measures to be employed was influenced by the following criteria:
Accessibility: the measurement should be accessible, or potentially made accessible, to
developers or researchers not otherwise familiar with psychophysiological evaluation; it
should not be restricted to clinical or hospital environments, such as function magnetic
resonance imaging (fMRI).
Ease of deployment: researchers and developers not previously familiar with the
measurement should be able to be trained in its deployment.
Rate of employment within existing psychophysiological play experience literature: the
measurements chosen should mirror those typically used in extant literature.
Applicability of the measure to the evaluation of the video game experience: it should
not restrict necessary movement, such as hand movement, or impede visual senses;
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similarly, it should be deployable within a computer or console lab environment (see
‘Accessibility’), and not overly obtrusive to the participant (as in the case of subcutaneous,
or needle, electrodes).
Complementary: the instruments themselves should be compatible with one another—
for example, they should not physically overlap one another.
Associated financial and temporal costs: the financial and temporal costs (such as set-
up) should be reasonable for general adoption within both research and industry.
EEG, ECG, EMG OO, EMG CS and EDA were chosen for their potential contributions to expanding current understanding of the psychophysiology of the optimal play experience, as well as for their adherence to the criteria established for selecting the psychophysiological approach. With the exception of EEG, all measures were non-mobile and required a wired connection to amplifiers. Some potential measures considered for inclusion were ultimately discarded due to incompatibility with other measures, or inappropriateness or infeasibility within wider research and industry contexts. These measures included EMG ZM, respiration and salivary cortisol. In the case of EMG ZM, the location of the ZM muscle was determined to be infeasible due to the inability to adequately prepare the site (e.g., shave) for analysis; measurement of respiration through a respiratory belt proved to overlap with ECG electrodes, introducing a potential for the dislodgement of electrodes or movement artefacts; and salivary cortisol was deemed impractical for commercial research contexts due to hygiene concerns. Finally, other measures—such as fMRI and functional near-infrared spectroscopy (fNIRS)—were not considered due to expense or incompatibility with video game play.
A tonic approach was chosen to allow for assessment of the overall player experience.
While the use of phasic analysis has facilitated the evaluation of physiological responses in player
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experiences (Ravaja et al., 2008) and been recommended for continued assessment of response to specific in-game stimuli (Nacke et al., 2010a), the average tonic overview of the player experience has been identified as capable of yielding suitable results for the analysis of player experiences
(Nacke et al., 2010a). The tonic approach has also been used regularly in previous player experience research (Drachen et al., 2010; Harmat et al., 2015; Kivikangas, 2006; Kneer et al.,
2016; Mandryk et al., 2006b; Nacke & Lindley, 2009; Nacke et al., 2010a; Nacke et al., 2010b;
Russoniello et al., 2009) for both the insights it provides for the overall understanding of the player experience and the reduced time costs relative to a phasic approach (Mandryk et al., 2006b).
Within psychophysiology, phasic assessment of responses to specific stimuli is a favoured approach; however, Stern et al. (2001, p. 50) warn that ‘this emphasis on stimulus-contingent responses and relative neglect of tonic levels is analogous to not seeing the forest for the trees’.
Due to these time costs, in association with the large sample size, a phasic analysis was determined as beyond the scope of this research (although it nonetheless presents an opportunity for exploration in future research).
3.3.3.1 Electroencephalography
The potential contributions of EEG to an understanding of the optimal play experience are extensive due to the insights the measurement allows into cortical activity. Of particular interest to player experience evaluation are the possible relationships that may be identified between the play experience and EEG frequency bands, allowing for the exploration of associated mental states such as attentional focus, information processing, mental workload and emotional function in video game play.
A dry cap EEG headset was chosen due to its relative ease of deployment, applicability to a video games research environment and unobtrusive nature. While wet cap EEG arrays allow for
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direct contact with the scalp and are generally used in clinical settings, the set-up procedure is involved, often time-consuming and can cause discomfort to the participant due to residual electrode gel being left in their hair. The EEG device chosen for this program of research was the
EMOTIV Epoc+, a 14-channel wireless EEG that employs electrodes—soaked with saline solution prior to use—with reusable electrode pads (see Figure X). The use of saline solution instead of electrode gel or paste is helpful in reducing participant discomfort. The EMOTIV Epoc+ is paired with recording software ‘EMOTIV TestBench’, allowing for the real-time recording of the raw EEG signal and the insertion of markers to represent events (such as the initiation or termination of a condition). The EEG signal is sequentially sampled at a rate of 128 samples per second (SPS).
While the set-up period of the headset was found to differ between participants due to variables such as hair volume, initial testing determined that set-up would typically not exceed five minutes in length. This situated the EMOTIV Epoc+ as a time-effective psychophysiological measurement during deployment, despite the temporal costs associated with data treatment and analysis (detailed in section 6.4.1). While not necessary for this program of research, the wireless nature of the EMOTIV Epoc+ headset allows for its deployment in more mobile research contexts—for example, the evaluation of exercise games that may require greater range of movement. The headset was also chosen for its generalisability to wider research and play-testing contexts.
A frequency analysis of the EEG data was chosen due to the interpretability of the frequency bands in terms of player experience. The absolute power of the alpha, beta and theta frequency bands were assessed for the associated cognitive states’ applicability to the assessment of optimal and sub-optimal play conditions.
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The association of alpha activity with ‘relaxed wakefulness’ (Davidson et al., 2000) and relative lack of cognitive processes (Stern et al., 2001) offers the potential to investigate alpha differences between a boring, or non-stimulating, play experience and stimulating ones; furthermore, decreases in alpha activity have been previously identified in players as indicating increased mood (Russoniello et al., 2009.) It was expected that beta activity, associated with alertness, mental activity and responses to cognitive task demands (Andreassi, 2007, p. 69;
Fernandez, Harmony, & Rodriguez, 1995; Ray & Cole, 1985), would also reveal interesting differences between optimal and sub-optimal play experiences. Finally, as increased theta activity has been previously associated with increased challenge in play experiences (Salminen & Ravaja,
2007), this represented a natural path for further analysis in the context of manipulated challenge‒ skill balance; furthermore, as theta activity has been simultaneously associated with states of drowsiness and attentional focus (Stern et al., 2001, p. 81), additional investigation of this band in a player experience context may expand upon psychophysiological understanding.
As delta activity only emerged in adults during slow wave sleep, this band was excluded from analysis (Stern et al., 2001, p. 81). As some studies have suggested that gamma activity may be a by-product of electromyographic interference (such as ocular movement), rather than indicative of cognitive activity, this band was also excluded from analysis (Whitham et al., 2007;
Whitham et al., 2008); however, future research may benefit from exploring this frequency band in the context of player experience analysis.
Finally, the sites chosen for analysis were the AF4 (frontal) and O2 (occipital; rear) right hemispheric sites (see Figure 12, p. 50). The AF4 site was chosen as beta activity—associated with mental processing and alertness, above—has been identified as most evident in the frontal cortex (Nacke, 2010), and has consequently been the focus of evaluating cognitive activity in
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player experience research (Nacke, 2010; Johnson et al., 2015; Russoniello et al., 2009). The O2, a site located on the occipital lobe, was chosen due to the association with increased occipital theta and challenge in a previous psychophysiological evaluation of the player experience (Salminen &
Ravaja, 2008). In the interest of time constraints, no other sites were included in analysis; however, these represent a path for analysis in future research. The reference channels for the EMOTIV
Epoc are in the P3 and P4 locations.
3.3.3.2 Electrodermal Activity
As EDA is widely regarded as one of the most easily deployable and interpretable measures in psychophysiological analysis (Stern et al., 2001), this measure was chosen due to its potential for generalisability to both research and industry environments. EDA was also chosen due to its demonstrable robustness as a measurement of physiological arousal. The prevalent employment of EDA in video game biofeedback research also establishes it as a measure relevant to player experience evaluation contexts.
The EDA system employed for this program of research was supplied by BIOPAC
Systems Inc., and consisted of an EDA amplifier that uses a constant voltage of 0.5 V to measure skin conductance, disposable snap 27 mm x 36 mm Ag/AgCl electrodes with a pre-filled gel cavity and unshielded electrode leads 1 m in length. The unshielded leads were chosen due to the use of an EMG ground electrode positioned directly in the centre of the forehead, as recommended by
Fridlund and Cacioppo (1986). EDA data is recorded and analysed in BIOPAC’s AcqKnowledge, software paired with all BIOPAC systems; as in EMOTIV’s TestBench software, AcqKnowledge records raw psychophysiological data in real time and allows for the insertion of markers to indicate events.
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The sites chosen for EDA analysis were the hypothenar and the thenar eminences of the palm. While the distal phalanges feature the greatest concentration of eccrine sweat glands, as established in section 2.8.7.1., this site was disqualified in this study due to the potential for excessive movement artefacts associated with rapid and continual finger movement during computer gaming.
Set-up time for the EDA system was not found to exceed five minutes, partially due to the minimal preparation required for the EDA electrodes—unlike with EMG and ECG, no abrasion is necessary; participants are simply required to wash their hands. The pre-filled disposable electrodes also aided in the brevity of the set-up period. Despite the compact set-up period, a further five minutes is required to elapse prior to recording to allow for successful skin‒gel contact to be established (Braithwaite et al., 2015).
3.3.3.3 Electromyography
As facial EMG analysis provides an opportunity to explore both positive and negative valence, this measure appeared uniquely applicable to investigating both optimal and sub-optimal play experiences. Originally, three sites were chosen for EMG analysis: the OO and ZM, both associated with enjoyment and positive valence, and the CS, associated with frustration and negative valence. Preliminary informal testing of the EMG measurement indicated that facial hair would prove intrusive in obtaining ZM activity, requiring either the removal of facial hair— discounted due to ethical considerations—or the exclusion of participants with facial hair. Due to the prominence of facial hair among the potential participant pool, and the remaining presence of a secondary measure of positive valence (OO), ZM was ultimately removed from the methodology.
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As with EDA, the EMG system employed was supplied by BIOPAC Systems Inc., and consisted of an EMG amplifier, 8mm Ag-AgCl shielded electrodes of the cup variety and 19 mm double-sided adhesive collars for adhesion to the skin. The electrode cups were filled with a non- irritating, hypo-allergenic gel conductant (‘SignaGel’). Impedance between electrodes was checked using a UFI Model 1089 MkIII Checktrode.
The set-up period for EMG was variable and dependent on the success of the initial set- up, as determined by the impedance. If the Checktrode reported unsatisfactory impedance levels
(> 10kΩ), the electrodes would be removed and the skin preparation process restarted. Initial informal testing found that the set-up period would not exceed 15 minutes in length.
3.3.3.4 Electrocardiography
The relationships identified between time pressure, stress, anxiety, and HRV and R-R analysis were identified as potentially revealing in the investigation of optimal and sub-optimal play experiences, and particularly pertinent in application to an action horror video game. The inclusion of ECG provided a secondary measure of arousal. ECG was also chosen due to its rate of employment within both play experience literature and development in biofeedback.
As with all other psychophysiological measurements except EEG, the ECG system was supplied by BIOPAC Systems Inc. The system consisted of an ECG amplifier, and used the 8 mm
Ag-AgCl shielded electrodes, collars and conductant gel used in EMG set-up. As with EMG, impedance between the electrodes was tested using the MkIII Checktrode.
The set-up period, although again dependant on achieving an impedance level of less than
10kΩ, did not exceed 10 minutes during informal testing. If acceptable impedance was obtained on the first application, the set-up would typically not exceed five minutes in length.
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HR and HRV analyses, measures of arousal and both psychological and physiological stress were chosen for the evaluation of ECG data. HR analysis in particular has represented a locus of research in player experience research (Ballard & West, 1996; Drachen et al., 2010;
Fleming & Rickwood, 2001; Harmat et al., 2015; Kneer et al., 2015; Lim & Reeves, 2010;
Mandryk et al., 2004, 2006b; Nacke et al., 2011; Ravaja et al., 2006; Weber et al., 2009; Wu &
Lin, 2011), and as such is situated as a valuable analysis approach within the space. While HRV is not typically assessed to the same extent, it may allow for a more nuanced understanding of psychophysiological stress in player experience analysis (Keller & Bless., 2008; Harmat et al.,
2015; Russoniello et al., 2009). For this research, the peaks of the HF component—a direct measure of cardiac parasympathetic activity—of HRV were assessed. While LF/HF and LF values were obtained, they were not evaluated for this program of research due to the limitations of LF analysis as a measure of sympathetic activity (Houle & Billman, 1999).
3.3.4 Selection of a Video Game Artefact
It was crucial that the video game artefact selected would support the aim, research questions and conditions of the research program. Several requirements were established for the selection of an appropriate video game for the study, as follows:
(i) representative of contemporary successful commercial video games and likely to
induce flow (and later, when flow was revealed as having possible issues, as
described in sections 3.4.4 and 4.3, likely to induce additional states such as
enjoyment, presence, autonomy and competence)
(ii) customisable—alterable to the extent of being able to develop both flow (optimal)
and low-flow (sub-optimal) game levels; this requirement also restricted the game
artefact to PC, due to the limited availability of ‘modding’ for console games
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(iii) allowing for uninterrupted eight-minute play sessions to ensure adequate time for
a tonic psychophysiological experience to emerge
(iv) allowing players to ‘jump in’—no prior investment in the game required (players
did not have to be familiar with the game narrative, or to have completed a certain
amount of the game)
(v) sufficiently intuitive to be enjoyed by participants of all skill and experience
levels following their exposure to a short tutorial.
On the basis of these requirements, the video game chosen was Valve Corporation’s first- person shooter zombie horror Left 4 Dead 2 (2009) (see Figure 15). Left 4 Dead 2 primarily features gun-based combat with enemy zombies in a post-apocalyptic setting, accompanied by three AI teammates (replaced with humans in multiplayer), with additional mechanics throughout that require players to complete certain tasks—for example, fuelling a generator to lower a bridge.
As well as standard zombie enemies, the game also features ‘special enemies’—zombies with unique abilities that can incapacitate players and require strategy to defeat.
Figure 15. Screenshot of Left 4 Dead 2 (Valve, 2009).
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With more than four million copies sold in the year of its release (Chiang, 2011) and an average rating of 89/100 on Metacritic, a review aggregator website often referenced in games literature (Baldwin, Johnson, & Wyeth, 2014; Phillips, Johnson, & Wyeth, 2013), it was determined that Left 4 Dead 2 was representative of a contemporary successful commercial video game. Furthermore, Left 4 Dead 2 was heavily modifiable, featuring a developer-supported
‘modding’ tool and source kit and an established modding community. Initial testing of levels within the game revealed several potential candidates for a minimum eight-minute play session.
Finally, the intuitive and recognisable nature of the game—a first-person shooter in which the players must shoot zombie enemies—allowed players ‘jump in’ without extensive prior experience, assisted by help tips that would explain objects or events as they were first encountered.
Left 4 Dead 2 was also chosen due to its native inclusion of DDA in the form of an entity known as the ‘AI Director’. DDA provides real-time adjustment of a game’s difficulty in response to player status to ensure challenge‒skill balance, and has proven successful for invoking flow in prior studies (Keller & Bless, 2008). It also allows for players of differing skill levels and game familiarity to enjoy similar play experiences, as the optimal play condition adjusts automatically to player ability. As the AI director operates primarily by manipulating the environment and resources available to the player, this form of DDA aligns with the implementation suggested by Negini et al. (2006).
3.3.5 Design Phase One
Initial artefact design followed the approach undertaken by Nacke and Lindley (2008).
The Boredom condition incorporated a linear level with weak opponents and repeating textures, no winning condition, a limited choice of weapons, a high amount of health and ammunition
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supplies, and a lack of surprises (enemies would not hide or attack from behind). To achieve repeating textures and models within a linear level, the map was designed in the form of a simple corridor without environmental clutter. Character conversations (friendly AI engaging in chatter with one another that reveals story) were removed. Other changes from the standard game to facilitate boredom focused on manipulating game challenge, and include the following:
(i) enemy AI altered to react slowly, or occasionally not at all, to player presence
(ii) no special zombies (powerful zombies with unique abilities present in the default
game)
(iii) no DDA
(iv) notably diminished enemy health, in that the player need only shoot them once
(v) player health unable to drop below 90%
(vi) high amount of ammunition and health pack supplies
(vii) only one available gun
(viii) no ‘winning condition’—the corridor does not end.
The Balance (flow) condition required very little modification to the original game, but the game was nonetheless altered to remove what early pre-testing revealed to be luck-based game changers. These were two ‘boss’ type zombies, known within the game as the ‘Witch’ and the
‘Tank’. The Witch (see Figure 16) had a chance of spawning at any time, or not at all, in any game level; interaction with the Witch also required prerequisite knowledge that the novice players may not intuit. Should the player not adopt the appropriate strategy (approaching quietly from behind, or avoiding entirely), the Witch was capable of killing the player and all AI teammates, thus prematurely ending the play condition.
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The Tank (see Figure 16), while less lethal and more intuitive than the Witch, was a disruptive event capable of spawning multiple times per play condition, dependent on player performance. As a ‘mini-boss’, the time resources required to dispatch the Tank were considerable. Due to the semi-random number of spawns, consistent play experiences could not be guaranteed between participants and thus necessitated removal.
Figure 16. Left 4 Dead 2 (Valve, 2009): Tank and Witch boss enemies.
With the exception of the removal of these enemy types, the Balance condition was unchanged from the version found in the official campaign. To this end, game difficulty was set to ‘normal’ in the map editor. Common enemy zombies had 50 health, and would spawn in herd sizes respective to the player performance, as judged by the AI Director. The player and their AI- controlled teammates had 100 points of health each, and would take 2 damage per hit to their front and 1 damage per hit to their back. The AI Director (DDA) was enabled. See Figure 18 for a screenshot of the typical play experience for the Balance condition. The map level ‘The Parish’, a winding maze-like cemetery, was chosen due to its high average completion time, under the assumption that players would not be able to complete the level before the allotted play session time expired.
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3.3.6 Design Phase One: Artefact Design Flaws
Both conditions were rejected after initial informal pilot testing among colleagues and collaborators revealed that flow was unnecessarily confounded with aesthetic quality, and that the desired playtimes were not being achieved. While challenge‒skill imbalance was achieved through direct manipulation of combat difficulty, aesthetic experience was compromised by the removal of environmental assets in the Boredom condition. As it would be difficult to separate the effect of reduced flow from that of reduced aesthetic quality, this condition was discarded in favour of developing a boredom condition derived only from challenge‒skill imbalance (low flow), with all environmental assets and character chatter intact. The Balance condition revealed errors in map selection, as expert players were able to complete the map in less than the required playtime of eight minutes and novice players easily became lost.
3.3.7 Design Phase Two
The second attempt at artefact design followed the framework established by Nacke and
Lindley (2008) only in regards to the manipulation of challenge‒skill balance through combat difficulty; Keller and Bless’ (2008) singular focus on challenge‒skill balance largely provided the framework for this design phase. Assuming challenge‒skill balance to be a central antecedent of flow, all conditions directly manipulated this balance. To avoid confounds associated with aesthetic differences, or potential differences in experiences if exposed to different environments, all play conditions now took place within the same map. The map selected was the ‘The Port’, in which players are required to fetch 16 canisters scattered throughout the map. Initial informal pilot testing revealed that players were unable to complete the condition in fewer than eight minutes for any condition, thus ensuring that no participant encountered a win condition.
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With the reintroduction of aesthetic elements to boredom in the form of a new map (‘The
Port’), the Boredom condition now featured the same environmental assets, textures and character conversations as the Balance condition. The Boredom condition continued to use all challenge manipulations detailed in section 3.3.5, with the exception of the introduction of the gas canister collection task. As the gas canisters are highlighted in the world in all conditions, rendering them easy to locate, the task of collecting the canisters was not inherently challenging; additionally, the large size of the map and the distribution of the canisters ensured a repetitive experience. For an example of the typical Boredom condition experience, see Figure 17. The simple level design of
‘The Port’ made navigation considerably easier.
Figure 17. Screenshot of Boredom condition (second iteration).
As with the first artefact design, the majority of level manipulation was undertaken to reduce the likelihood of participants’ experiencing flow in the low-flow conditions; due to the presence of DDA, it was expected that the game was capable of inducing flow without modification in the
Balance condition. The Tank was reintroduced to the Balance condition due to technical limitations; while the Tank could be prevented from spawning, testing revealed that the game
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would still play the associated music and sounds. The Tank was thus introduced in a much diminished capacity—whereas defaults Tanks possess 4000 health, the modified Tanks for this condition had only 1000 (five times that of a common enemy type) and were easily dispatched by the player. See Figure 18 for a screenshot of the typical play experience within the Balance condition.
Figure 18. Screenshot of Balance condition.
The second design phase saw the addition of the Overload condition. The inclusion of this condition enables a complete evaluation of the challenge‒skill balance spectrum: the measurement of skill > challenge (Boredom), matched challenge‒skill (Balance), and challenge > skill
(Overload). To this end, game difficulty was set to ‘expert’ in the map editor. Common enemy health was raised from 50 (in the Balance condition) to 1000. The player and their AI-controlled teammates had 100 points of health each, and would take 20 damage per hit to their front and 10 damage per hit to their back. All special enemies, including the ‘Tank’, had their health multiplied by four; this gave Tanks 16,000 health points. In addition to this, zombies were more likely to spawn behind the player—there was no game-enforced limit on how many zombies could be
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present in the map at a time, and the special infected spawn rate was increased. DDA was disabled
for this condition. See Figure 19 for a screenshot of the typical play experience for the Overload
condition. For an overview of the condition manipulations in Design Phase 2, see Table 3.
Figure 19. Screenshot of Overload condition.
Table 3. Game condition differences (second iteration) Boredom Balance Overload Common Impeded Standard Extreme Special enemies Disabled Standard Extreme
Canister Enabled Enabled Enabled collection DDA Disabled Enabled Disabled
Some features of Left 4 Dead 2 were altered for all conditions to preserve experimental
integrity and inhibit potential confounds. These alterations include the following:
The achievement system was disabled to ensure that no players experienced a reward for
achieving something that other players did not (for example, killing five ‘Hunter’-type
special enemies).
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Sprays, player-chosen images that can be ‘pasted’ into the game world by pressing a
button, were also disabled.
Players were restricted to only one of four playable characters (‘Nick’).
Weapon choice was eliminated from the game. In the default game, players may choose
to play with a sniper rifle, machete, assault rifle, chainsaw and so on. To ensure a similar
experience across all conditions and experiments, only the assault rifle as the primary
weapon and the pistol as the secondary weapon were enabled.
3.3.8 Tutorial
Prior to pilot testing of the second methodology design, a tutorial was created for the purposes of introducing participants to the game mechanics, rules and environments. This was undertaken with the intent of minimising the influence of learning effect and orienting response
(Stern et al., 2001), familiarising participants with the control scheme and input requirements of the game, and reducing uncertainty in the tested play conditions.
The tutorial was developed within same map (‘The Port’) as the play conditions to maintain consistency between game exposures. Pop-up speech bubbles throughout the tutorial were inserted to guide participants throughout the map and provide graphic instructions for which keys were needed for certain actions (see Figure 20). Throughout the tutorial, participants were introduced
(in order) to the following:
the actions required to pick up and equip a gun, ammunition and health pack
a single non-aggressive enemy, with instructions for how to shoot it
how to pick up fuel canisters, and pour their contents into the generator (the objective
within all play conditions)
how to track fuel canister silhouettes through the map
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a single aggressive special enemy (‘Hunter’), with instructions for how to shoot it
time allotted for idle exploration, so as to allow a further period of acclimation to the
environment and controls.
The tutorial was scripted to ensure that the participant could complete all required actions in fewer than five minutes, and allow a further two minutes for general exploration of the map. To adequately guide the participant through the tutorial, some areas that were otherwise open to participants in the play conditions were restricted through the insertion of fences; the only way to access these regions was through a single path that required the completion of all tutorial objectives.
Figure 20. Screenshot of tutorial.
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3.4 STAGE 2—STUDY 1 (PILOT) AND REVISION
Pilot test of refined video game artefact; further modification to Boredom condition; exploration of subjective constructs expanded beyond flow; development of additional experiment software
3.4.1 Introduction
The second phase of artefact design was piloted in an adaptation of the final proposed methodology, without psychophysiological measurement. Psychophysiological measurement was excluded from the pilot test, as the primary objective was to determine the success of the level design in both achieving the optimal experience of flow (as in the Balance condition), and inhibiting it (as in the Boredom and Overload conditions). After each condition, participants answered self-report surveys addressing flow. Successful invocation of flow, as gauged by the validated Long Flow State Scale (FSS-2), would allow for the exploration of RQ1a and RQ1a.
3.4.2 Methodology
The final methodology featured a within-subjects study design, employing semi- counterbalanced video game artefacts to minimise order effect. Self-reported flow was evaluated as a method for determining the conditions’ successes in both inhibiting and disinhibiting the optimal play experience of flow, as well as the conditions’ rigour in manipulating challenge‒skill balance and imbalance.
3.4.2.1 Semi-Counterbalanced Approach
While the study design employed some counterbalancing to control for order effects, the conditions were not fully counterbalanced. The decision was ultimately predicated on the risk to prolonged emotional or physiological response to the Overload condition; early internal play- testing within Design Phase 2 (section 3.4) revealed the condition to be frustrating or
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overwhelming, potentially influencing participant mood and receptiveness to subsequent conditions. The limitations of this approach are discussed in section 6.5.
3.4.3 Study
The study took place between August and September 2014, and data was collected from
20 participants. The results from this study, discussed in Chapter 4, informed further design revisions and refinements for the game artefact, experiment procedure and study design. These iterations are discussed in the following section.
3.4.4 Revisions to Methodology
As described in section 4.3, the Overload condition performed as expected in terms of showing lower levels of flow. However, expected differences between the Balance and Boredom conditions were absent, prompting a reconsideration of the existing methodology. The program of research was adapted to these findings in three ways:
further modification of the Boredom condition to remove existing potential for
engagement or enjoyment through challenge‒skill manipulation
refocusing of the psychological constructs investigated in the program of research; a
widening of scope allows for the investigation of other psychological constructs, as
identified within games research literature, beyond flow
while flow is still to be evaluated, its role is to be pared down.
3.4.4.1 Continued Modification of the Boredom Condition
The final iteration of the Boredom condition removed enemies and combat entirely; the gameplay consisted exclusively of retrieving the gas canisters scattered throughout the map (see
Figure 21). Despite the removal of combat altogether, the inclusion of canister collection and travel in the virtual world ensured that the game remained sufficiently game-like.
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It was possible that the (limited) inclusion of zombie targets, despite their lack of threat to the player, was still sufficiently engaging as to inhibit the potential for boredom within the condition. As discussed in sections 4.3.1 and 4.3.2., it was also possible that the anticipation of future enemies may have prevented disengagement from the game. In an effort to ensure the
Boredom condition was capable of inducing boredom, the combat in the game was removed altogether.
The various iterations of the video game artefact conditions are detailed in Table 4. Please note that the Overload condition was not introduced until Iteration Two. For video of these conditions, refer to Appendix G.
Figure 21. Boredom condition (second iteration), feat. no combat.
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Table 4. Game condition differences (third iteration)
Iteration One Boredom Balance Overload Map Custom ‘corridor’ The Parish - Common enemies Impeded Standard - Special enemies Disabled Standard - Tank Disabled Disabled - Canister collection - - - DDA Disabled Enabled -
Iteration Two Boredom Balance Overload Map The Port The Port The Port Common enemies Impeded Standard Extreme Special enemies Disabled Standard Extreme Tank Disabled Impeded Extreme Canister collection Enabled Enabled Enabled DDA Disabled Enabled Disabled
Iteration Three Boredom Balance Overload Map The Port The Port The Port Common enemies Disabled Standard Extreme Special enemies Disabled Standard Extreme Tank Disabled Impeded Extreme Canister collection Enabled Enabled Enabled DDA Disabled Enabled Disabled
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3.4.4.2 Refocusing of Psychological Constructs
As discussed in section 4.3, it is also possible that low flow is either a) inherently difficult to obtain in immersive, aesthetic video game conditions, b) not reliant on the obtainment of challenge‒skill balance or c) not able to be accurately measured by the FSS-2 within this context, particularly where the skills of the player exceed the challenges of the game. It was thus decided to withdraw from flow as the sole psychological construct for psychophysiological evaluation; instead, the optimal and sub-optimal play experiences—as obtained through optimal and sub- optimal challenge‒skill balance—would also provide a lens through which to examine other psychological constructs of note in games literature. The scope of the research program was thus broadened to include evaluation of presence/immersion, enjoyment, affect and features of SDT, as well as flow (in a reduced capacity).
3.4.5 Development of Sequencing Software
The development of sequencing software was undertaken prior to the commencement of data collection for the final study. This software, dubbed ‘Sequencer’, was developed and designed in collaboration with a colleague in QUT’s Computer‒Human Interaction department. The development of Sequencer was undertaken with the primary aim of reducing participant‒ researcher interaction, identified by Mandryk et al. (2006) as a potential confound in the recording of psychophysiological measures.
Sequencer is an application that makes use of Microsoft’s .NET functionality. The program’s primary functionality is that it can follow event branches based on conditioned logic
(i.e., the software takes a coded participant number and follows a sequence of steps associated with that condition). Before each experiment, the experiment coordinator can input a participant
ID and set up the program for the participant front-end. Using the participant number, the
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experiment sequencer determines which branch of the experiment to run and loads surveys, game environments and baseline applications in the coded sequence. From the launched state, the participants can commence the study by clicking on simple ‘Next’ buttons. Unlike traditional
Windows applications, the environment obfuscates the task bar of the Windows environment to intentionally remove participant access to the system clock, and to prevent participants from accidentally exiting the software environment. A secondary feature of the program is inbuilt timers that control the length of the game conditions and the baseline application, ensuring an identical playtime across participants.
While Sequencer was successful in minimising participant‒researcher interaction, it also had several secondary benefits. In the absence of the program, the researcher would have had to necessarily launch all programs and surveys themselves, thus causing delaying marking the initiation of experiment events in the psychophysiological recording software (or an additional member of the research team would have needed to be present to assist in this task). It also maximised physical distance, allowing the researcher to sit at a machine located beyond participant eyesight; the participant may thus have been less likely to feel as though their video game play and performance were being watched, which potentially influenced their physiological response. Sequencer had the additional benefit of reducing the chance for human error during experiment runtime. The complex nature of the procedure (involving three computers, five programs and five psychophysiological measures), and the requirement for precision, were at odds, and so benefitted from the inclusion of the Sequencer software. Finally, Sequencer had the ability to gracefully recover from an unexpected error in experiment runtime (for example, a computer crash); in the event of abrupt experiment failure, correct order was resumed through the manual selection of the last completed sequence of the experiment. See Figure 22 for a screenshot of the Sequencer software’s main menu.
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Figure 22. Screenshot of Sequencer main menu.
3.4.5.1 Baseline Application
A simple application that renders the entirety of the computer screen black was developed as the ‘task’ for the psychophysiological baseline. Baselines were timed to terminate at one minute and 30 seconds. This application was launched by Sequencer throughout the experimental procedure.
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3.5 STAGE 3—STUDY 2
Final study design and methodology; data collection
3.5.1 Introduction
The final iteration of the video game conditions and experimental methodology was deployed for data collection over a period of 13 months. This final methodology allowed for the exploration of both the aim of the research program, and the investigation of research questions
RQ1a and RQ1b. The expansion to include multiple subjective constructs beyond flow enabled a more nuanced exploration of the relationship between psychophysiological and subjective response; furthermore, the iteration of both the video game artefacts (conditions and tutorial) and assistive experimental software (Sequencer and baseline program) allowed for a robust study design that supported the psychophysiological method by minimising participant‒researcher contact and automation of timed events.
3.5.2 Methodology
The final methodology featured a repeated-measures within-subjects study design, employing semi-counterbalanced video game artefacts to minimise order effect. A psychophysiological and subjective approach was undertaken through use of both the subjective‒ quantitative survey method and objective‒quantitative psychophysiological method.
Throughout the study, participants’ subjective responses were obtained through the use of three validated scales: the PENS, the Short Flow State Scale (S FSS-2), the interest/enjoyment subscale of the Intrinsic Motivation Inventory (IMI), the Self-Assessment Manikin (SAM) and the
Positive and Negative Affect Schedule (PANAS). Two custom items were also included to ask plain questions such as, ‘How fun did you find this session?’ and ‘How did your ability to play the game match the challenges of the game?’ For this thesis, analysis of subjective measures was
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limited to the PENS, S FSS-2 and IMI scales. Finally, five psychophysiological measures were used to obtain cortical, cardiovascular, sweat gland and facial muscle activity. For a complete discussion of the method and measures used, see Chapter 5.
3.6 ETHICS AND LIMITATIONS
Both studies in this program of research were evaluated as low risk, with no added risk beyond standard interaction with video games and technology. Participants were informed of the game’s classification category (MA15+) and content prior to participating in the study in the recruitment materials, participant information consent sheet and pre-experiment briefing. This allowed for those uncomfortable with first-person shooters to not participate or otherwise rescind interest. Furthermore, the participant pool was limited to those aged 17+ in consideration of the game’s classification, and to best satisfy low-risk human ethics requirements at QUT. Due to concerns for the consistency of data gathered from ECG, individuals with a history of heart arrhythmia were selected against and thus requested to not participate in the recruitment materials.
While there was a chance that participants would feel tension or discomfort during the set- up of the psychophysiological measures, they were informed as to the nature of the physiological measurements in the same recruitment and information materials. Any potential participants who may have felt uncomfortable with the concept of psychophysiological measurement were thus granted the opportunity at multiple stages to not participate or remove themselves from the study.
Participants were also advised to alert the researcher to any discomfort they experienced throughout the study, and were informed during the pre-experiment briefing that they could withdraw at any time.
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Finally, the use of hypoallergenic soaps and gels throughout the study minimised the chance of allergic reactions. All electrodes were thoroughly cleaned, disinfected and rinsed between experiment sessions for the dual purposes of hygiene and equipment maintenance.All responses and data were anonymised and stored in secure environments, accessible only on password- protected computers. This data was made available only to the researchers involved in the study, as detailed in participant information consent sheets. The signed information consent sheets were stored in a lockable cabinet on university property.
Both studies were approved by the QUT University Human Research Ethics Committee
(approval #1300000796). The second full study was granted approval as an amendment to the original documentation submitted for the pilot study.
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3.7 STAGE 4—ANALYSIS AND INTERPRETATION
Data treatment, analysis and interpretation.
3.7.1 Introduction
The final step of this research program was the treatment, analysis and interpretation of all psychophysiological and subjective data. The results were intended to underpin the methodological and knowledge contributions made to player experience research, and allow for the comprehensive exploration of RQ1a and RQ1b.
3.7.2 Scope
Upon completion of data collection, all psychophysiological measures were treated, cleaned and analysed in compliance with approaches detailed in sections 2.8.7 and 5.1.8. This represented a notable time investment within this research program; associated implications for using psychophysiological measures in assessing player experience are discussed in section 6.4.1.
Analysis was then undertaken in a series of one-way MANOVAs and ANOVAs, revealing significant differences between the optimal and sub-optimal conditions across both subjective and psychophysiological measures. The results confirm the success of the conditions in the creation of optimal and sub-optimal conditions, informing the interpretation of the psychophysiological results. The results are reported in sections 5.2 and 5.3.
The interpretation of results was undertaken through the lens of both player experience and psychophysiological research; while player experience components offered reasonable explanations for many results revealed for the physiological responses, some results may have emerged as a consequence of basic psychophysiological principles (e.g., habituation). The strength of the results is also investigated through effect sizes—see sections 5.5 and 5.6 for these discussions. The results are assessed in terms of the thesis aim and research questions in section
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6.3. The employment of psychophysiological evaluation within this program of research is critically assessed, allowing for the identification of research limitations, in section 6.5. In sections
6.4 and 6.5, recommendations are made for psychophysiological methodologies in player experience research with an eye to future research opportunities.
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4 STUDY 1: PILOT
Study 1 was undertaken to explore the effectiveness of the play condition artefacts in inhibiting and disinhibiting the experience of flow. A three-condition approach allowed for the investigation of flow through challenge‒skill balance, low flow through challenge > skill imbalance (frustration/anxiety) and low flow through skill > challenge imbalance (boredom). The successful development of three conditions matching these criteria enabled a comprehensive psychophysiological exploration of a concept synonymous with optimal experiences within both player experience and psychological research spaces (Sweetser & Wyeth, 2005; Csikszentmihalyi,
1990).
It was expected that the results from this preliminary study would inform ongoing design of the play conditions and experiment procedure, as well as the broader paradigms of the research program. The culmination of these developments facilitated the commencement of the final study, including psychophysiological measures, and enabled the consideration and exploration of RQs
1, 1a and 1b.
4.1 METHOD
4.1.1 Recruitment
The desired sample size for the pilot study was 20 participants. This offered opportunity for the preliminary analysis and evaluation of the subjective response to condition, as well as insight into the general success of the three play conditions in either inhibiting or disinhibiting the optimal play experience through flow and challenge‒skill balance manipulation. Recruited individuals were aged 17 and older (see section 3.6). Participants were recruited from the Bachelor
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of Games and Interactive Entertainment cohort at QUT and through snowball sampling.
Compensation for participation was a game software key, which granted free access to a library of games developed by Valve Corporation, and the opportunity to enter a draw to win a $50 gift voucher.
4.1.2 Measures
4.1.2.1 Demographics Questionnaire
The demographics questionnaire (see Appendix D) included age, gender, level of experience with video games, level of experience with first-person shooters and estimated number of hours spent playing Left 4 Dead 2 and 1 (with no prior experience with the game required for participation in the study).
4.1.2.2 Long Flow State Scale
The FSS-2 (Jackson & Eklund, 2002) is a 36-item survey measured on a 5-point Likert scale, with ‘1’ representing ‘strongly disagree’ and ‘5’ representing ‘strongly agree’. The scale consists of subscales (four items each) measuring each of the eight components of flow—skill, concentration, clear goals, unambiguous feedback, action‒awareness, sense of control, loss of self- consciousness and transformation of time—and a subscale measuring the autotelic experience associated with flow. Scores are calculated for each of the nine subscales and for total flow (the nine subscales combined). The FSS-2 is a validated measure for evaluating the experience of flow in various settings, and has previously been successfully applied to video (Kivikangas, 2006;
Vella, Johnson, & Hides, 2013; Harmat, 2015). See Appendix A for sample items.
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4.1.3 Laboratory
The experiments took place within a temperature-controlled computer laboratory at QUT.
Each computer within the laboratory is divided by partitions into an individual ‘booth’, providing privacy in the use of each machine. The computer used in the experiment is a corner booth, chosen so that the participant is unable to be seen (or unable to see) the outside hallway through the door window. The researcher sits at a machine behind and diagonal to the participant, and does not face the participant’s screen, again encouraging privacy in the participant’s use of the machine. The laboratory is lit artificially, with no outside-facing windows that would introduce natural lighting.
During data collection, only the participant and the researcher were present in the laboratory. See
Figure 23 for the laboratory configuration.
Figure 23. Experimental laboratory. Left: participant desk, feat. partitions; right: view of participant desk from researcher desk.
4.1.4 Procedure
The experimental sessions took place in the computer laboratory detailed in section 4.1.3, with only one participant tested per one-hour session. Each experiment session took place between
10 am and 6 pm, with most sessions occurring on weekdays. After providing informed consent,
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participants were advised to be aware of a copy of the Left 4 Dead 2 control scheme mounted on the partition walls.
Participants were then directed to answer questions regarding demographics and previous experience with both video games in general and Left 4 Dead 2 specifically. Once this was completed, participants then played a four-minute structured tutorial for the game that exposed them to the mechanics and requirements they needed to be aware of for gameplay. Finally, participants played three 10-minute game sessions in semi-counterbalanced order
(Boredom/Balance/Overload, or Balance/Boredom/Overload), with a five-minute questionnaire segment punctuating each game condition. Once the experiment was completed, participants were debriefed and thanked for their time. See Figure 24 for a depiction of the experiment procedure.
Control Background Introduction Scheme Tutorial 00.00 - 03.00 Survey Diagram 7.00 - 11.00 03.00 - 6.00 6.00 - 7.00
First Play Second Play Session First FSS-2 Session Second FSS-2 (Bore/Bal) 21.00 - 26.00 (Bore/Bal) 36.00 - 41.00 11.00 - 21.00 26.00 - 36.00
Third Play Debriefed and Session Third FSS-2 51.00 - 56.00 Thanked (Overload) 56.00 - 60.00 41.00 - 51.00
Figure 24. Study 1: Experiment procedure.
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4.1.5 Participants
Twenty participants participated in the study, 95% male and aged between 17 and 31
(mean = 20.2, SD = 3.24). On a Likert scale of 1–7, with ‘7’ representing ‘extremely experienced’ and ‘1’ ‘not at all experienced’, participants self-rated as a mean of 6 (SD = 1.12) for ‘general experience with video games’ and 5.3 (SD = 1.56) for ‘experience with first-person shooters’.
Participants reported between one and 80 hours spent playing video games per week, with a mean of 25.45 hours (median = 20, SD = 20.05). In terms of familiarity with Left 4 Dead franchise, participants were asked how many hours they had spent playing both Left 4 Dead 1 and Left 4
Dead 2. Eleven participants had never played Left 4 Dead 1 before; the remaining nine had played between 1 and 100 hours (mean = 20.78, median = 10, SD = 30.93). In terms of Left 4 Dead 2, 11 had never played before, with the remaining 9 having played between 2 and 150 hours (mean =
32.56, median = 10, SD = 48.84). Overall, 60% of participants had previously played a Left 4
Dead title.
4.2 FINDINGS
A within-subjects MANOVA was conducted using gameplay condition (Boredom,
Balance or Overload) as the independent variable and all outcome measures (the nine subscales as well as total flow) as dependent variables. All statistical assumptions of MANOVA were met, with the exception of univariate outliers identified on the challenge‒skill balance and transformation of time subscales from a single participant. No substantive differences in results were found with outliers removed, and in the interest of statistical power, the results reported here include all cases. Using Wilk’s Lambda revealed a statistically significant effect of condition on the combined dependent variables (Λ = 0.259, F(18,60) = 3.221, p < 0.005; partial η2 = .491).
Univariate follow-ups revealed differences in terms of challenge‒skill balance (F(2,38) = 13.744,
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p < .005), merging of action and awareness (F(2,38) = 13.744, p = .007), and sense of control
(F(2,38) = 4.552, p = .017). Total flow was also found to differ significantly between conditions
(F(2,38) = 4.867, p = .013).
Pairwise follow-up tests using Bonferroni corrections were conducted on these three subscales and total flow. Challenge‒skill balance was significantly higher for the Boredom condition than for the Overload condition (p = .008); it was also significantly higher for the
Balance condition than for the Overload condition (p < .005). The Boredom condition also scored significantly higher than the Overload condition in the merging of action and awareness (p = .005) and sense of control subscales (p = .046). Finally, for total flow, no difference between the
Boredom condition and the Balance condition was identified (p = .097), but the Balance condition revealed significantly greater flow than the Overload condition (p = .017). For a visualisation of these results, see Figures 25 and 26. Overall, the results show that greater total flow was experienced in the Boredom condition than in the Overload condition. This difference seems to be a function of participants reporting relatively high levels of challenge‒skill balance, merging of action‒awareness and sense of control in the Boredom condition. Additionally, no significant differences were found between the Boredom condition and the Balance condition.
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Figure 28. Study 1: Flow State Scale total flow results.
Figure 27. Study 1: Flow State Scale subscale results
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4.3 DISCUSSION
4.3.1 Difficulties in Reducing Flow in Immersive Games
A proposed explanation for the absence of differences in flow experiences between the
Boredom and Balance conditions is the potential for flow experience regardless of challenge‒skill imbalance in immersive video games, suggesting that a challenge‒skill imbalance does not necessarily negate flow in immersive video games. As Left 4 Dead 2 features a highly detailed environment, it is possible that some participants may have derived aspects of flow (such as altered perception of time, focused concentration and loss of self- consciousness) from world exploration.
In other words, regardless of very low levels of challenge offered by the enemies in the game, participants were able to achieve flow by exploring and/or enjoying the aesthetic qualities of the game world. As many commercial titles feature detailed environments, this may point to issues with the analysis of flow experienced in video games.
Expanding on the concept of engagement through immersion or aesthetic quality, it is also possible that the challenge > skill imbalance achieved by the Overload condition was successful in preventing flow by disinhibiting immersion within the world. This may be due to repeated player deaths, limited mobility (constrained by enemies) and reduced chance for exploration.
The potential challenges of reducing flow in immersive games highlighted risks in the exclusive measurement of flow as a tool for psychophysiological comparison. Therefore, the decision was made to expand the program of research to assess other psychological constructs associated with the player experience; for further discussion of this, see section 3.4.4.
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4.3.2 Unsuccessful Condition Design
The absence of any discernible differences in the flow experience between the Boredom and Balance conditions may also indicate errors in condition design. It may be that participants were still sufficiently challenged by the demands of the Boredom condition, despite the range of constraints introduced to ensure very low levels of challenge. Specifically, player health never fell below 90%, enemies would die from a single shot, no ‘special infected’ would spawn and a low enemy spawn rate was used (less than one-third of that seen in the flow condition). Regardless, it may be that participants found unexpected ways to seek challenge in the Boredom condition—for example, they may have felt challenged as a function of not being sure when enemies would appear
(the anticipation of enemies in some ways balancing the relative lack of enemies), or perhaps by obtaining as many fuel canisters as possible before play time concluded in the condition
(confirmed anecdotally by two participants to the researcher). It is also possible that the presence of combat was still perceived as sufficiently threatening or challenging by the players, regardless of the reality of the challenge; this may have been influenced by anticipation of continued combat or anticipation of elevating combat difficulty.
These conclusions directly prompted additional iterations to the play condition designs; in particular, Boredom was revised to minimise the risk of perceived challenge. While this still allowed the potential for player-created challenge, it was determined that such a possibility was unavoidable within the context of computer games. The Boredom condition was predominately redesigned to remove combat altogether; for extrapolation on this, see section 3.4.4.
4.3.3 Challenge‒Skill as an Antecedent
Keller and Landhäußer (2012) and Csikszentmihalyi et al. (2005) identify challenge‒skill balance as an antecedent of flow. However, Fong et al. (2014) note that this balance between
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challenge and skill may be commonly associated with, but not always necessary for, flow to occur.
It may thus be that the experience of flow is possible despite challenge‒skill imbalance; in this case, sole focus on challenge‒skill balance may not have been adequate for successful flow manipulation. Rheinberg et al. (2003) propose that flow is divisible into two factors: ‘absorption by activity’ and ‘fluency of performance’. In this approach, flow through absorption is often associated with balanced or slightly challenging activities, whereas flow through fluency is stronger under low-challenge activities. It is possible that participants experienced flow through absorption in the Balance condition, and flow through fluency in the Boredom condition.
As the methodology was revised to include the assessment of additional subjective player experience constructs, the role of challenge‒skill balance as an antecedent to flow was no longer essential for the program of research. The manipulation of challenge‒skill balance still provided a useful tool for comparison among play experiences; furthermore, due to the importance of challenge and challenge‒skill balance in the player experience, challenge‒skill manipulation remained capable of promoting and inhibiting optimal experiences. For discussion on this, see section 2.3.1.
4.3.4 Scale Applicability
By contrasting a flow experience with a boring experience, this study raises the question of the FSS-2 scale’s applicability to some video game experiences. The FSS-2 is a commercial scale, and specific scale items cannot be published; the relevant subscales as a whole are discussed here.
Two of the subscales in question—the ‘merging of action and awareness’ subscale, and the ‘sense of control’ subscale—present the possibility of answering the question of scale applicability in a manner congruent with experiences of both flow and boredom or disengagement.
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The ‘merging of action and awareness’ subscale contains items focused on performing actions automatically and without much thought; the ‘sense of control’ subscale contains items focused on feeling control over what one is doing. While these experiences are true of the flow experience, they are also arguably true of a boring or unchallenging experience. As the Boredom condition was not mentally taxing, it follows that participants did not need to put ‘too much thought’ into their actions; similarly, as the game is not mechanically challenging and was selected for its intuitiveness, it is likely that participants generally felt particularly confident in their control of the Boredom condition. This aligns with Keller and Bless’s (2008) findings, in which the highest levels of perceived control were reported for the boredom condition used in their study. It may be that video games otherwise not likely to induce flow still offer participants the opportunity for high levels of sense of control and merging of action‒awareness. In this way, the FSS-2 may indicate high levels of flow in video games with these features.
It is particularly notable that no significant difference was found between the Balance and
Boredom conditions for the challenge‒skill balance subscale. A possible explanation stems from the subscales including items that could be interpreted as asking if the respondent has sufficient skills to meet the presented challenge. Participants could have sensed that their skills were enough
(or more than enough) to meet the challenges of the Boredom condition, leading to high scores on this subscale. The results from the current study raise the possibility that the FSS-2, when applied to boring or exceptionally easy video game scenarios, could result in high ratings of flow when flow may not actually be occurring.
As in section 3.4.1, the potential limitations of using this measure were minimised by shifting focus from the exclusive physiological assessment of flow. As limitations in the applicability of the FSS-2 to sub-optimal game experiences were identified, the subjective
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assessment of flow was pared down to the Jackson S FSS-2—an abridged nine-item version of the
FSS-2. This allowed for the ongoing psychophysiological assessment of flow, and the introduction of additional subjective measures, without notably lengthening survey time commitments in the experimental process.
4.4 CONCLUSIONS
The development and evaluation of the flow and low-flow experimental play conditions has uncovered difficulties concerning operationalising and measuring flow in games research, and raises questions regarding the role of challenge‒skill balance in flow. Study 1 highlights the difficulties inherent in manipulating flow: as aesthetically ‘immersive’ experiences are common attributes of commercial games, continued research must remain cognisant of this as a potential confound with flow. As for the Boredom condition used in the current study, it was uncertain whether people experienced flow regardless of the low challenge experienced in the level, or whether higher challenge was created or perceived by participants in unexpected ways. Finally, the applicability of Jackson et al.’s FSS-2 scales to play experiences—especially sub-optimal experiences—is potentially limited, particularly in the context of challenge‒skill manipulation.
These findings informed the final iteration of the video game conditions and study methodology, primarily in the redesign of the Boredom condition and the introduction of additional psychological constructs.
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5 STUDY 2—MAIN STUDY
Both chapters 3 and 4 have demonstrated the iterations in the design and development of the final study. These steps were informed by recommended practice in the psychophysiological space, insights gathered from contemporary player experience literature and findings gathered from the preliminary pilot study. These iterations culminated in creating and refining study software artefacts (play conditions, tutorial, Sequencer and baseline program), developing a robust and relevant psychophysiological method and establishing a final subjective approach to allow for a simultaneous biometric and psychometric investigation of the player experience. The culmination of these developments allowed the final study to commence and to encompass psychophysiological measures, thus enabling the consideration and exploration of the RQs 1, 1a and 1b.
1. How effectively can the psychophysiological method be used to evaluate the player
experience?
a. What are the differences in psychophysiological response between optimal and
sub-optimal play experiences?
b. Which psychophysiological measures, or combination of psychophysiological
measures, most reliably predict specific components of the player experience as assessed
by subjective measures?
Evaluating the psychophysiological response to the Boredom, Balance and Overload conditions facilitated an exploration of differences in response, and a determination of which physiological processes may be associated with optimal or sub-optimal play experiences, thus addressing RQ1a. The concurrent analysis of the subjective experience—employing flow,
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enjoyment, autonomy, competence and presence as the self-reported psychometrics for evaluation—also enabled a manipulation check to determine the game conditions’ success in evoking (or inhibiting) optimal play experiences.
Furthermore, exploring the psychophysiological response as a predictor of subjective response in statistical analysis allowed for the investigation of RQ1b. This enabled a greater understanding of components of the player experiences and which psychophysiological measures most effectively predict specific components of the player experience. This, in turn, provided guidance regarding these components in future research and commercial contexts.
5.1 METHOD
5.1.1 Recruitment
The desired sample size was 90 to 150 participants aged 17 and older, which facilitated the analysis and evaluation of the psychophysiological and subjective response to condition, as well as the analysis of psychophysiology as a predictor of subjective response via multiple regression testing (Tabachnick & Fidell, 2007). This sample size in particular was chosen as it would allow for inferential statistics such as multiple regression testing, which would allow insight regarding the relative predictive power of different physiological measures. Participants were recruited from the Bachelor of Games and Interactive Entertainment cohort at QUT, the wider university undergraduate student base through targeted social media pages, the general public via gaming forums and social media, and through snowball sampling. Compensation for participation was a game software key, which granted free access to a library of games developed by Valve
Corporation, and the opportunity to enter a draw to win a $50 gift voucher.
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5.1.2 Self-Report Measures
5.1.3 Demographics Questionnaire
The demographics questionnaire from the study undertaken in Stage 3 (section 4.1.2) was reused for this study.
5.1.4 Short Flow State Scale
In lieu of Jackson and Eklund’s FSS-2 (2002) employed in Stage 2, the subjective focus on flow was pared back to instead employ the S FSS-2 (Jackson & Eklund, 2002). This exchange was undertaken in consideration of time constraints for the experimental procedure, and reflected the shift in research program objectives from flow to multiple subjective states.
The S FSS-2 features a 9-item scale instead of the 36 items present in the previous scale.
Like the FSS-2, the S FSS-2 items are measured on a 5-point Likert scale with ‘1’ representing
‘strongly disagree’ and ‘5’ representing ‘strongly agree’. Each item evaluates one of the eight components of flow—skill, concentration, clear goals, unambiguous feedback, action‒awareness, sense of control, loss of self-consciousness and transformation of time—with an additional item assigned to measuring the autotelic experience associated with flow.
5.1.5 Player Experience of Needs Satisfaction
The development of the PENS scale has allowed researchers to explore the three needs of
SDT (autonomy, competence and relatedness) through a measure uniquely developed for the experience of video game play (Ryan et al., 2006). The PENS also evaluates presence and intuitive controls, core influences on the player experience (Ryan et al., 2006), as separate subscales.
Presence, as discussed in section 2.5, is theorised to be positively associated with increased intrinsic motivation. Intuitive controls assists in enabling feelings of competence, autonomy (by
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not stymieing the player with awkward or difficult controls) and presence (by facilitating feelings of ‘being there’ through to providing an intuitive control scheme that does not require active consideration) (Ryan et al., 2006). The PENS survey thus features five distinct subscales: autonomy, competence, relatedness, presence and intuitive controls.
The efficacy of the PENS survey was investigated across four studies, which found that the measure was successful in determining needs satisfaction and play motivation (Ryan, Rigby,
& Przybylski, 2006). The PENS survey has since been employed and validated in digital games literature in several contexts, including as support for investigating player wellbeing (Vella,
Johnson, & Hides, 2013) and exploring personality and game genre preferences (Johnson &
Gardner, 2010), and as a consistent tool for player experience analysis (Brühlmann & Schmid,
2015).
Three of the needs satisfaction subscales were included in the study design: competence, autonomy and presence, which are thought to be core components of the player experience (Ryan,
Rigby, & Przybylski, 2006). The PENS scale has been shown to be a statistically reliable measure
(Johnson & Gardner, 2010); while it contains a subscale for relatedness, this was excluded from the study on the grounds that the game is a single-player experience and no NPCs were present in the game. Similarly, the intuitive controls subscale was excluded from the study, as the control set-up was not manipulated in any way.
Items were rated on a Likert scale of 1–7 (‘7’ representing ‘strongly agree’), and are as follows (also included in Appendix B):
Competence: ‘I felt very capable and effective when playing.’
Autonomy: ‘I did things in the game because they interested me.’
Presence: ‘I experienced feelings as deeply in the game as I have in real life.’
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5.1.6 Intrinsic Motivation Inventory
Predicated on SDT, the IMI is a scale for measuring subjective experiences in relation to an activity. It is considered a reliable measure of its subscales: interest/enjoyment, competence, effort, value, pressure and perceived choice (autonomy). An additional subscale has since been added, though has not yet been validated (relatedness). Of these subscales, only interest/enjoyment is a direct measure of intrinsic motivation, and so features the greatest number of items (Ryan &
Deci, 2000). As the IMI and PENS are similarly rooted in SDT, it was determined that including the needs satisfaction subscales from the IMI would be largely repetitious, and that those subscales
(competence, autonomy, relatedness) should not be included in the study design. However, the interest/enjoyment subscale of the IMI does not have an analogue in the PENS, and was used to gauge the interest and enjoyment of participants across the experimental conditions. Items were rated on a Likert scale of 1–7 (‘7’ representing ‘very true’), and are as follows (see Appendix C for all items):
‘I would describe this activity as very interesting.’
‘I enjoyed doing this activity very much.’
5.1.7 Psychophysiological Measures
The psychophysiological measures employed for this study—EMG (OO), EMG (CS),
EEG, EDA and ECG—were identified in section 3.3.3 and selected for their adherence to criteria of accessibility, ease of deployment, high employment rate within games literature, applicability to player experience evaluation, affordable temporal and financial costs, and complementarity with one another. These measures were chosen early in the study design to ensure that the final methodology would remain feasible and congruent with the psychophysiological measures’
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limitations and requirements (e.g., as the instruments were largely non-mobile, participants would not be able to move from their desks after the set-up period).
5.1.8 Ethics
Ethical approval was sought and granted by the QUT ethics committee (approval
#1300000796); see section 3.6
5.1.9 Procedure
The experimental sessions took place in the computer laboratory detailed in section 4.1.3, with only one participant tested per two-hour session. Each experiment session took place between
10 am and 6 pm, with most sessions occurring on weekdays. After providing informed consent, participants were given the opportunity to use the bathroom before the experiment session commenced. Participants were then taken to a sink within the laboratory and asked to wash their hands in preparation for the EDA electrodes. Upon drying their hands, participants were seated at a PC in a corner booth.
The electrodes and instruments for the psychophysiological measures were then applied over a duration of approximately 30 minutes. In some instances, application could take longer if it was determined that the impedance between the electrodes was too high, or the electrodes were not providing a clean physiological trace. EMG and ECG sites were appropriately prepared prior to application with the use of an abrasion gel, gauze wipe and alcohol wipe. EEG electrodes were saturated with saline prior to application.
Participants were then directed to answer questions regarding their demographics and previous experience with both video games in general and Left 4 Dead 2 specifically. They then played a four-minute structured tutorial for the game that exposed them to the mechanics and requirements they would need to be aware of for gameplay. They then played three 10-minute-
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and-30-second game sessions in semi-counterbalanced order (Boredom/Balance/Overload, or
Balance/Boredom/Overload). The Sequencer program would automatically deliver participants the appropriate surveys after each session. A two-minute baseline was also delivered by Sequencer at the start of the experiment, in between in each play session and after all the play sessions were completed.
Once the experiment was completed, the psychophysiological equipment was removed from the participant. Participants were offered a wet wipe to clean any remaining electrode gel from their person, and were then verbally debriefed and thanked. See Figure 27 for a visual summary of the experimental process.
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Background Hands Introduction Washed EDA Setup 00.00 - 05.00 Survey 12.00 - 17.00 05.00 - 10.00 10.00 - 12.00
EMG (CS) EMG (OO) ECG Setup EEG Setup Setup Setup 31.00 - 38.00 38.00 - 43.00 17.00 - 24.00 24.00 - 31.00
Re- Baseline Tutorial Baseline application 50.00 - 52.00 52.00 - 57.00 57.00 - 59.00 43.00 - 50.00
First Play Second Play First Surveys Baseline Session 70.00 - 77.00 77.00 - 79.00 Session 59.00 - 70.00 79.00 - 90.00
Second Third Play Third Baseline Surveys 97.00 - 99.00 Session Surveys 90.00 - 97.00 99.00 - 110.00 110.00 - 117.00
Equipment Baseline Debriefed 117.00 - 119.00 Removed 124.00 - 126.00 119.00 - 124.00
Figure 29. Experimental procedure.
5.1.10 Attachment of Psychophysiological Measures
All physiological measures were prepared and attached in accordance with standard procedures in the psychophysiological space, as detailed in section 2.8.7. This section describes the individual attachment processes employed in this study for each physiological measure.
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5.1.11 Electrodermal Activity Attachment
As discussed in section 5.1.6, all participants washed their hands at a sink provided in the laboratory prior to the attachment of the disposable EDA electrodes. Sink water was heated so to prevent reactionary restriction of capillaries or skin response to cold temperatures; as an additional prevention measure, participants washed with hypoallergenic goat’s milk liquid pump soap out of consideration for both hygiene and minimising the potential for an allergic reaction. Once washed, participants would then dry their hands with paper towel under instruction to ensure that their palms did not remain damp. Participants were then guided back to their chair, whereupon the attachment of all psychophysiological measures occurred. Two disposable EL507 snap electrodes were attached to the thenar and hypothenar regions of the palm (see Figure 32) respectively, and secured with medical tape to reduce the risk of movement or detachment throughout the experiment. The real-time EDA recording within the AcqKnowledge recording and analysis software was then visually verified to ensure connection and an uninterrupted EDA trace. EDA was attached first to allow for the 10-minute ‘settle’ time to occur throughout the attachment of the additional psychophysiological measures.
The GSR100C Bioamp module settings for EDA recording were as follows:
GAIN: 5µΩ/V LP: 10Hz HP: DC HP: DC.
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5.1.12 EMG Attachment Participants’ skin was topically abraded in the left OO, left CS and grounding regions; to ensure that each muscle was accurately located, participants were asked to smile (OO) and frown
(CS) while the experimenter held a cotton bud against the target site. Once the muscle was located, the skin was abraded for approximately 60 seconds with Nuprep Skin Prep Gel applied to a cotton bud to strip the top layer of skin particles. The gel was then thoroughly wiped away with gauze pad, doubling as a secondary abrasion process. The abraded sites were further cleaned with an alcohol wipe, removing any remaining product or skin debris.
Prior to the participant’s arrival, adhesive collars were attached to four shielded EL254s
4mm Ag-AgCl cup electrodes (for use in measuring EMG OO and EMG CS) and a single unshielded EL254 4mm Ag-AgCl cup electrode (for employment as a grounding electrode). Once the EMG skin regions had been prepared, the electrode cup cavities were filled with hypoallergenic Signa conductive gel; this process was delayed until immediately prior to attachment to prevent the gel drying. The single unshielded EL254 ground electrode was then fixed to the centre of the forehead; similarly, a pair each of the EL254S shielded electrodes were next attached to the OO and CS regions (see Figure 28). Electrode leads were tucked behind the participant’s left ear to prevent vision obstruction. All electrodes were secured with medical tape.
Finally, impedance between both the OO electrode pair and CS electrode pair was checked using the UFI Model 1089 MkIII Checktrode; in the event of unsatisfactory impedance levels (> 10kΩ), the electrodes were removed and the skin preparation and application process restarted with new electrodes.
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The electrode leads fed into an extension module that was clipped to the participant’s shirt collar, as the original lead length for the electrodes was too restrictive of movement. As with EDA, the EMG recordings were checked against AcqKnowledge to ensure an uninterrupted connection.
The EMG100C Bioamp module settings for EMG recording were as follows:
Figure 30. EMG placement. EMG CS electrodes placed on brow, EMG OO electrodes placed near eye. Right: Ground electrode visible in centre of forehead.
GAIN: 2000 LP: 500 Hz HP: OFF HP: 1.0 Hz.
5.1.13 Electrocardiography Attachment
The electrode sites for the two-lead ECG recording followed the same abrasion procedure outlined above in section 5.1.7.2. The sites chosen were approximately three to five centimetres below the right clavicle, and on the lower left-hand side of the ribcage (approximately three centimetres above the lowest ribcage bone, or roughly in alignment with the elbow); see Figure
29. All participants wore a two-piece outfit (e.g., trousers and a shirt) to the experiment, in
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compliance with a request given in the recruitment materials, to ensure appropriate access to these regions.
As with the EMG, as outlined in the previous section, EL254s 4mm Ag-AgCl electrodes were prepared, but unfilled, prior to the participant’s arrival to avert complications that could arise with dried electrode gel. Once the sites had been prepared, the electrode gel was added to the cup cavities and attached to the participant. Both electrodes were then secured with medical tape and fed into an extension module clipped to the participant’s shirt collar. Impedance was then checked on the UFI Model 1089 MkIII Checktrode, with impedance levels below 10kΩ necessitating removing the electrodes and restarting the preparation and application process.
As with EMG and EDA, recordings were checked against AcqKnowledge to ensure an uninterrupted connection. In some cases, large amounts of adipose tissue prevented the obtainment of an adequate signal from the ECG electrodes. If this issue continued after removal and reapplication of new electrodes, the experiment proceeded in the interests of time restraints, with
ECG data marked for later removal from the sample.
Figure 31. ECG placement.
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The ECG100C Bioamp module settings for ECG recording were as follows:
GAIN: 500 MODE: NORM LPN: ON HP: 0.5Hz.
5.1.14 Electroencephalography Attachment
Prior to the participant’s arrival at the laboratory, the EEG headset’s Epoc 14 felt sensor pads were moistened with a saline solution. Additionally, between experiment sessions, the sensor pads were kept in a hydrator pack to minimise drying. Immediately prior to attaching the EEG headset, the sensor pads were again briefly remoistened. Finally, in the continued absence of a stable or present connection with specific electrode sites, the participant’s scalp was topically doused with the saline solution.
Once attached to the participant’s scalp, the EEG headset was adjusted to ensure adequate contact with all electrode regions. This information was provided by the EMOTIV Epoc
TestBench recording software: if connection was established, the circle representing the relevant electrode site remained blank. Colour signified contact quality in all other instances: red, orange, yellow and green circles indicated contact quality in ascending order (red indicated the lowest contact quality and green indicated the highest). Data collection only occurred once all electrode sites displayed as green. See Figures 30 and 31 for the contact quality display and headset placement.
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Figure 35. TestBench contact Figure 34. EEG placement. quality display.
5.1.15 Data Treatment
For this program of research, physiological data was evaluated tonically over the course of each play session. A tonic analysis approach was selected for analysis to allow for an overview of the player experience. Phasic analysis thus represents an additional path for analysis in future research. Tonic analysis was also found to be congruent with this program’s research questions concerning evaluation of utility in employing psychophysiological measures to predict subjective states that reflect the entirety of the play experience. Further discussion on this approach is detailed in sections 6.4 and 6.5.
All physiological data was acquired on two programs installed on a machine removed from the participant’s field of view. Recording times were manually flagged and timestamped within both programs by the researcher as they occurred (e.g., when a play session began, when a play session terminated, when a baseline began and so on). Although each play session was 10 minutes and 30 seconds in length, only 10 minutes of each play session was analysed; 20 seconds were removed from the start of the play session and 10 seconds removed from the conclusion.
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This was to allow participants a grace period to physically settle or adjust in the first 20 seconds of play, and to allow the researcher a delay when flagging the end of the play session during the last 10 seconds.
All physiological data underwent a rigorous cleaning process prior to its inclusion for statistical analysis, as detailed in the following sections. These also describe the treatment and analysis methods used per psychophysiological measure.
5.1.16 Electrodermal Activity
EDA activity (mS) was recorded in BIOPAC’s AcqKnowledge 4.2 data acquisition and analysis software. Once collected, the data were manually inspected for movement artefacts, noise interference and interrupted signals. From the full sample size of 89, four cases were removed from analysis due to largely compromised or non-existent data. It is speculated that this loss of data occurred as a consequence of an electrode shifting or detaching from the site; in particular, this may have transpired due to sweat eroding the adhesion of both the electrode and the tape. In all cases, movement artefacts were visually evaluated in windows (timebins) of 10-second epochs.
If an artefact was found to influence two or more seconds of the epoch, the full 10-second epoch was removed from analysis. As with all measures, any cases that contained over 15% of data loss were removed from analysis.
The values corresponding with the 10-minute play sessions, with movement and noise artefacts handled through null value replacement, were then exported into SPSS for analysis. A total mean value of each 10-minute play session was derived per n.
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Figure 36. EDA placement.
5.1.17 EMG OO and CS
EMG activity (µV) was recorded in BIOPAC’s AcqKnowledge 4.2 data acquisition and analysis software. Once collected, the data was manually inspected for movement artefacts, noise interference and interrupted signals. For EMG OO, 31 cases were removed from analysis due to largely compromised or non-existent data; for EMG CS, 54 cases were removed from analysis. In all instances of removed cases, the EMG trace indicated either a loss of signal or a signal compromised by an excess of noise (see Figure 33). This may have occurred either as a consequence of lost contact between the electrode and electrode site during data collection, poor electrode contact or faulty equipment. In instances where the contaminated data was minor, visual inspection and handling of artefacts occurred as in the treatment of EDA data (see section 5.1.16).
For the analysis and treatment of EMG data, a 10 Hz high-pass filter using a Hanning window was first applied to the entire data channel; this was undertaken for the purpose of clarifying the EMG data through filtering out of most eye movement and blink artefacts. The
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average rectified EMG was then derived from each channel individually at intervals of 0.5 seconds. A mean epoch analysis, using 10-second epoch widths with zero time lapse between epochs, was performed on the rectified data. The values corresponding with the 10-minute play sessions, with movement and noise artefacts handled through null value replacement, were then exported into SPSS for analysis.
Figure 38. EMG data comparison. Top left and bottom indicate noisy or weak signals, respectively; top right indicates usable/uncompromised data. Top images both indicate single 10-second epochs, bottom image indicates 2 x 15-second epochs.
5.1.18 Electrocardiography
ECG activity was recorded in BIOPAC’s AcqKnowledge 4.2 data acquisition and analysis software. Once collected, the data was manually inspected for movement artefacts, noise interference and interrupted signals. Of the full sample size of 89, nine cases were removed from analysis due to compromised or non-existent data. As with EDA, it is speculated that this loss of data occurred as a consequence of an electrode shifting or detaching from the site. In some cases, a consistent ECG signal was unobtainable with the available electrodes due to excess adipose tissue in the lower abdominal region.
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Once visual inspection of the data was completed, the 10-minute time bins corresponding with play sessions were separated and imported into Kubios HRV 2.2 HR variability analysis software (Tarvainen, Niskanen, Lipponen, Ranta-oho, & Karjalainen, 2014). Mean R-R, HR, HF peaks (Hz), and LF/HF analyses were performed (HR and HF peaks were evaluated within this program of research). The window width used for the fast Fourier transform was 256 samples, with a window overlap of 50%. The frequency bands used for frequency analysis were 0.04–0.15 for LF and 0.15–0.4 for HF. All analyses were undertaken using the Kubios software’s native artefact correction filter, set to the level ‘Very Strong’, which allowed for the correction of out- of-range R-R intervals and featured respiration frequency analysis that ensured the HF component remained within the HF band limits (Tarvainen et al., 2014). The final values for HF peaks (Hz) and HR were exported to SPSS for statistical analysis.
5.1.19 EEG
EEG activity (uV) was recorded in EMOTIV’s TestBench data acquisition software, with
BIOPAC’s AcqKnowledge 4.2 software employed for data treatment and frequency analysis. For the purposes of this research program, the AF4 and O2 sites were evaluated for their proximity to the frontal and occipital regions (see section 3.3.3.1). Absolute power for the frequency bands were derived within SPSS for the AF4 and O2 sites respectively, with average power estimates calculated using a fast Fourier transformation within AcqKnowledge’s frequency analysis. The thresholds used to identify each frequency band were as follows:
Theta: 4–8 Hz
Alpha: 8–13 Hz
Beta: 13–30 Hz.
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Upon frequency analysis output, each frequency band was visually inspected for movement artefacts, noise interference and interrupted signals. Of the full sample of 89, 22 cases were lost for the AF4 site and 20 cases were lost for the O2 site. As with EDA and ECG, it is speculated that a proportion of this data loss occurred as a consequence of electrodes shifting or detaching from the site; however, in some cases, regular interference throughout rendered much of the signal unusable. For cases with minor movement artefacts and interference, the severity and length of the artefact was manually assessed and replaced with null values when appropriate.
The mean absolute power values for each play condition per frequency band per electrode site were then imported into SPSS. A mean value was then derived from each of these for statistical analysis, producing a total of 18 separate EEG variables per participant (e.g., O2 Alpha for
Boredom, O2 Alpha for Balance, O2 Alpha for Overload and so on, for alpha, beta and theta across both sites).
5.1.20 Participants
Ninety participants participated in the study, but a single participant was removed from the final sample due to computer hardware failure prematurely terminating the experiment. Of the final sample size of 89 participants, 77.5% were male and 22.5% were female, all between the ages of 17 and 38 (mean = 23.41, SD = 4.53). On a Likert scale of 1–7, with ‘7’ representing
‘extremely experienced’ and ‘1’ ‘not at all experienced’, participants self-rated with a mean of
5.96 (SD = 1.33) for ‘general experience with video games’ and 5.16 (SD = 1.72) for ‘experience with first-person shooters’. Participants reported between 0 and 70 hours spent playing video games per week, with a mean of 21.79 hours (median = 20 hours, SD = 16.04). In terms of familiarity with the Left 4 Dead franchise, participants were asked how many hours they had spent playing both Left 4 Dead 1 and Left 4 Dead 2. Forty-five participants had never played Left 4 Dead
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1 before, with the remaining 44 having played between 1 and 120 hours (mean = 25.61 hours, median = 10 hours, SD = 36.48). As for Left 4 Dead 2, 34 had never played before, and the remaining 55 had played between 1 and 200 hours (mean = 32.47, median = 15 hours, SD = 43.43).
Overall, 69.66% of participants had previously played a Left 4 Dead title.
5.1.21 Analysis
To explore RQ1a, the subjective and psychophysiological response to the Boredom,
Balance and Overload conditions were evaluated. Subjective player experience measures and psychophysiological measures were analysed separately. The primary analysis was undertaken using a one-way MANOVA to determine the effect of condition (Boredom, Balance, Overload) on subjective player experience (flow, enjoyment, competence, autonomy and presence) and a combination of one-way MANOVAs and ANOVAs for the psychophysiological measures.
Specifically, the majority of the psychophysiological measures (EDA, HR, HF; all EEG frequencies) were assessed together in a single MANOVA, while EMG CS and EMG OO were each assessed in individual ANOVAs due to a notably diminished sample size as a consequence of data loss. The exploration of subjective response was undertaken as a manipulation check to evaluate the conditions’ respective success in either evoking or inhibiting an optimal play experience. The MANOVA approach offers an advantage in psychophysiological analysis in that it does not make the assumption of sphericity, which is rarely met in psychophysiological data
(Vasey & Thayer, 1987).
To explore how well psychophysiological response predicted subjective states (RQ2), two sets of multiple regression analyses were initially conducted. The first set focused on the participant’s psychophysiological response and player experience ratings in the Balance condition
(one regression equation was calculated for each subjective player experience construct, with
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psychophysiological measures entered as predictor variables). To allow for the possibility that some participants may have experienced play more positively (e.g., greater flow) in a condition other than Balance, a second set of regression analyses was conducted using each participant’s data from the condition in which they experienced the highest levels of the outcome measure (e.g., flow, interest/enjoyment, etc.). Based on the results of the regression analyses, further data exploration was undertaken using Pearson’s correlation coefficients and calculations of variability. All statistical analyses were undertaken using IBM SPSS Statistics 23.0.
5.2 SELF-REPORT RESULTS
5.2.1 Confirmation of Optimal and Sub-optimal Conditions
5.2.1.1 Assumptions and Outliers for Subjective Measures
Mahalanobis distance identified one multivariate outlier, and boxplots revealed two univariate outliers for flow (Boredom, Overload) and four univariate outliers for interest/enjoyment (Boredom) (Field, 2013). No substantive differences in the pattern of results were found with outliers excluded from analyses; in the interest of statistical power, the results reported here include all 89 cases.
Preliminary assumption-checking revealed abnormalities in data distribution, as assessed by the Shapiro-Wilk test (p > .05). The violations of normality occurred for the subjective ratings of flow in the Boredom (p = .036) and Balance (p = .014) conditions; ratings of interest/enjoyment in the Boredom condition (p = < .001); ratings of competence in the Boredom (p = .006), Balance
(p = < .001) and Overload (p = .001) conditions; ratings of autonomy in the Boredom condition
(p = .045); and ratings of presence in the Boredom (p = .012) and Balance (p = .048) conditions.
The analyses were subsequently run on both untransformed and transformed data; however (as
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might be expected given the robustness of the one-way MANOVA to deviations from normality
[Laerd, 2013]), the pattern of results remained the same, so for ease of interpretation, the untransformed results are reported in this section. There was no evidence of multicollinearity, as assessed by Pearson correlation; there were linear relationships, as assessed by scatterplots.
5.2.1.2 Findings
A repeated-measures MANOVA revealed a significant multivariate within-subjects effect of condition on flow, interest/enjoyment, competence, autonomy and presence using Wilk’s
2 Lambda (F(10, 79) = 26.807, p < .001, ηp = .772). Mauchly’s Test indicated that the assumption of sphericity had been violated for flow (W = .870, χ2(2) = 12.100, p < .005), interest/enjoyment
(W = .661, χ2(2)= 35.989, p < .001), competence (W = .887, χ2(2)= 10.417, p = .005) and autonomy
(W = .837, χ2(2) = 15.505, p < .001), and so a Greenhouse-Geisser adjustment (flow: ε = .885; interest/enjoyment: ε = .747; competence: ε = .899; autonomy: ε = .860) was used for these DVs in within-subjects univariate analysis. Sphericity was assumed for presence. Significant univariate
2 main effects were observed for flow (F(1.770, 155.773) = 34.634, p < .001, ηp = .282),
2 interest/enjoyment (F(1.494, 131.463) = 33.693, p < .001, ηp = .277), Competence (F(1.797,
2 2 158.153) = 85.387, p < .001, ηp = .492), autonomy (F(1.719, 151.301) = 27.332, p < .001, ηp =
2 .237) and presence (F(2, 176) = 29.529, p < .001, ηp = .251).
Post-hoc analysis revealed that for the main effect on flow, the Balance condition (M =
3.92, SD = 0.553) showed significantly higher flow levels than the Overload condition (M = 3.447,
SD = 0.588, p < .001); likewise, the Boredom condition (M = 3.931, SD = 0.472) showed significantly higher flow levels than the Overload condition (p < .001). No significant differences were revealed between the Balance and Boredom conditions (p > 0.999). For the main effect on interest/enjoyment, participants reported significant differences between all three conditions, such
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that the Balance condition (M = 4.728, SD = 1.146) showed significantly greater interest/enjoyment than both the Boredom (M = 3.47, SD = 1.185, p < .001) and Overload (M =
4.233, SD = 1.35, p < .001) conditions, and the Overload condition also showed higher interest/enjoyment than the Boredom condition (p < .001). For the main effect on competence, both Boredom (M = 5.045, SD = 1.374, p < .001) and Balance (M = 4.838, SD = 1.489, p < .001) showed higher levels of competence than Overload (M = 2.857, SD = 1.342), with no significant differences revealed between Boredom and Balance (p = .842). For the main effect on autonomy, the Balance condition (M = 4.247, SD = 1.316) showed significantly higher levels of autonomy than both the Boredom (M = 3.299, SD = 1.42, p < .001) and Overload (M = 3.326, SD = 1.253, p
< .001) conditions, with no significant differences revealed between the Boredom and Overload conditions (p > 0.999). Finally, the main effect on presence revealed significantly higher levels of presence in the Balance condition (M = 3.88, SD = 1.34) than in both the Boredom (M = 3.054,
SD = 1.231, p < .001) and Overload (M = 3.298, SD = 1.252, p < .001) conditions, with no significant difference revealed between Boredom and Overload conditions (p = .104). See Table
5 for a summary of these results, and Figures 34‒38 for visualisations of these results.
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Table 5. Summary of main effect on subjective response
Sample Size Mean SD Bore p Bal p Over p Flow n = 89 Boredom 3.931 0.472 < .001 Balance 3.92 0.553 < .001 Overload 3.447 0.588 < .001 < .001 Int/Enj n = 89 Boredom 3.47 1.185 < .001 < .001 Balance 4.728 1.146 < .001 < .001 Overload 4.233 1.35 < .001 < .001 Competence n = 89 Boredom 5.045 1.374 < .001 Balance 4.838 1.489 < .001 Overload 2.857 1.342 < .001 < .001 Autonomy n = 89 Boredom 3.299 1.42 < .001 Balance 4.247 1.316 < .001 < .001 Overload 3.326 1.253 < .001 Presence n = 89 Boredom 3.054 1.231 < .001 Balance 3.88 1.34 < .001 < .001 Overload 3.298 1.252 < .001
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Figure 39. Study 2 Short Flow State Scale results.
Figure 40. IMI Interest/Enjoyment results.
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Figure 41. PENS competence results.
Figure 42. PENS presence results.
Figure 43. PENS autonomy results.
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5.3 PSYCHOPHYSIOLOGICAL DIFFERENCES IN OPTIMAL AND SUB- OPTIMAL CONDITIONS
5.3.1 Assumptions and Outliers for Psychophysiological Measures
There were no multivariate outliers as assessed by Mahalanobis distance. However, box plot assessment revealed univariate outliers across conditions: three unique outliers were identified for EDA, two for HR, five for HF, seven for EMG OO and three for EMG CS.
Additionally, outliers were found at many of the EEG collection sites for one or more of the frequency bands; the number of outliers ranged from to four to 10 (for details, see Appendix F).
No substantive differences in the pattern of results were found with outliers for EDA, HR, HF and
EMG CS; analysis was thus performed on all cases. As outliers were found to be influencing the pattern of results for EMG OO and EEG, all analyses were performed on transformed versions of this data. As outliers remained in the transformed EEG data, a further four outliers identified as consistent across all sites and frequency bands were removed from analysis.
Preliminary assumption-checking revealed abnormalities in data distribution, as assessed by the Shapiro-Wilk test (p > .05). The violations of normality occurred for measures of HF in all conditions (p < .001); EMG CS in the Boredom (p = .028) and Balance (p = .039) conditions; transformed EMG OO in the Balance condition (p = .005); AF4 Alpha in the Balance condition
(p = .034); and AF4 Theta in the Overload condition (p = .012). The analyses were subsequently run on both untransformed and transformed data for HF and EMG CS; however, the pattern of results remained the same, so for ease of interpretation, the untransformed results are reported in this section. These are further supported by the robustness of the one-way MANOVA to deviations from normality (Laerd, 2013). There was no multicollinearity, as assessed by Pearson correlation.
Some evidence of non-linearity was identified for EMG OO, HF peaks and all EEG frequency
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bands across both sites, suggesting that analyses involving these variables had reduced statistical power (Laerd, 2013).
Due to disparities in sample sizes between measures, as discussed in sections 5.1.8.2,
5.1.8.4 and 5.1.10, analyses of these measures were conducted in a series of separate one-way
MANOVAs and ANOVAs. Analyses were thus performed on EDA, HR and HF in a single one- way MANOVA on 75 cases; all EEG sites and frequencies in a separate single one-way
MANOVA on 50 cases; and EMG OO and EMG CS in individual one-way RM ANOVAs, with
58 and 35 cases respectively.
5.3.2 Results for the One-Way Multivariate Analysis of Variance on
Electrodermal Activity, High Frequency and Heart Rate
A repeated-measures MANOVA revealed a significant multivariate within-subjects effect
2 of condition on EDA, HF and HR using Wilk’s Lambda (F(6, 69) = 9.041, p < .001, ηp = .440).
Mauchly’s Test indicated that the assumption of sphericity had been violated for HR (W = .891,
χ2(2) = 8.399, p = .015), and so a Greenhouse-Geisser adjustment (ε = .902) was used for this DV in within-subjects univariate analysis. Sphericity was assumed for EDA and HF. Significant
2 univariate main effects were observed for EDA (F(2, 148) = 6.538, p = .002, ηp = .081), HR
2 2 (F(1.804, 131.682) = 5.632, p = .006, ηp = .071) and HF (F(2, 148) = 8.396, p < .001, ηp = .102).
Post-hoc analysis revealed that for the main effect on EDA, the Overload condition (M =
15.101, SD = 6.65) showed significantly higher EDA levels than the Boredom condition (M =
14.107, SD = 6.616, p = .001); no significant differences were revealed between Overload and
Balance conditions (M = 14.689, SD = 6.61, p = .540) or the Balance and Boredom conditions (p
= .095). For the main effect on HR, analysis revealed significantly greater HR in the Boredom condition than in the Overload condition, such that the Boredom condition (M = 79.075, SD =
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13.284) showed significantly higher HR than the Overload condition (M = 76.571, SD = 13.564, p < .001); no significant differences were revealed between the Boredom and Balance conditions
(M = 78.046, SD = 13.194, p = .684) or the Balance and Overload conditions (p = .166). Finally, for the main effect on HF Peaks, the Overload condition (M = .208, SD = .0733) revealed significantly lower HF Peaks than both the Balance (M = .236, SD = .086, p = .018) and Boredom
(M = .248, SD = .078, p < .001) conditions; no significant difference was found between Balance and Boredom (p = .760). For a summary of these results, see Table 6; for a visualisation, see
Figures 39‒41.
Table 6. Summary of main effect on EDA, HR, and HF Peaks
Sample Size Mean SD Bore p Bal p Over p EDA n = 75 Boredom 14.107 6.616 = .001 Balance 14.689 6.61 Overload 15.101 6.65 = .001 Heart Rate n = 75 Boredom 79.075 13.284 < .001 Balance 78.046 13.194 Overload 76.571 13.564 < .001 HF Peaks n = 75 Boredom .248 .078 < .001 Balance .236 .086 = .018 Overload .208 .0733 < .001 = .018
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Figure 44. EDA results
Figure 45. HRV HF Peaks results
Figure 46. HR results
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5.3.3 Results for the One-Way Multivariate Analysis of Variance on
Electroencephalography
A repeated-measures MANOVA revealed a significant multivariate within-subjects effect of condition on all transformed EEG variables using Wilk’s Lambda (F(12, 38) = 35.850, p < .001,
2 ηp = .919). Mauchly’s Test indicated that the assumption of sphericity had been violated for AF4
Alpha (W = .833, χ2(2) = 8.767, p = .012), AF4 Beta (W = .827, χ2(2) = 9.126, p = .010), O2 Alpha
(W = .364, χ2(2) = 48.556, p < .001), O2 Beta (W = .246, χ2(2) = 67.318, p < .001) and O2 Theta
(W = .334, χ2(2) = 52.617, p < .001), and so a Greenhouse-Geisser adjustment (AF4 Alpha: ε =
.857; AF4 Beta: ε = .852; O2 Alpha: ε = .611; O2 Beta: ε = .570, O2 Theta: ε = .600) was used for these DVs in within-subjects univariate analysis. Sphericity was assumed for AF4 Theta.
Significant univariate main effects were observed for AF4 Beta (F(1.705, 83.536) = 7.793, p =
2 2 .001, ηp = .137), O2 Alpha (F(1.222, 59.889) = 24.767, p < .001, ηp = .336), O2 Beta (F(1.140,
2 2 55.872) = 31.171, p < .001, ηp = .389) and O2 Theta (F(1.201, 58.829) = 28.333, p < .001, ηp =
.366).
Post-hoc analysis revealed that for the main effect on transformed AF4 Beta, the Boredom condition (M = -2.474, SD = .163) showed significantly lower AF4 Beta than both the Balance (M
= -2.418, SD = .189, p = .008) and Overload (M = -2.428, SD = .174, p = .012) conditions; no significant differences were found between the Balance and Overload conditions (p > .999). For the main effect of transformed O2 Alpha, the Boredom condition (M = -2.608, SD = .185) showed significantly lower O2 Alpha than both the Balance (M = -2.547, SD = .183, p < .001) and
Overload (M = -2.571, SD = .169, p < .001) conditions; no significant differences were found between the Balance and Overload conditions (p > .999). For the main effect of transformed O2
Beta, the Boredom condition (M = -2.491, SD = .218) showed significantly lower O2 Beta than
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both the Balance (M = -2.404, SD = .232, p < .001) and Overload (M = -2.443, SD = .205, p <
.001) conditions; no significant differences were found between the Balance and Overload conditions (p = .261). Finally, for transformed O2 Theta, the Boredom condition (M = -2.506, SD
= .182) showed significantly lower O2 Theta than both the Balance (M = ‒2.448, SD = .169, p <
.001) and Overload (M = ‒2.465, SD = .163, p < .001) conditions; no significant differences were found between the Balance and Overload conditions (p > .999). No significant differences between conditions were found for the main effects on the AF4 Alpha and AF4 Theta DVs. See Figure 42 for a visualisation of these results.
Figure 47. EEG frequency band results (reversed).
5.3.4 Results for the One-Way Repeated-Measures Multivariate Analysis of
Variance on Electromyography - Orbicularis Oculi
As discussed in sections 5.1.8.2 and 5.1.10, EMG OO was excluded from the RM
MANOVA analysis of psychophysiological variables due to the disparity in sample size.
Therefore, in an effort to preserve statistical power for EDA, HR and HF, the transformed data for
EMG OO was assessed in a separate, one-way repeated-measures ANOVA.
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Mauchly’s Test indicated that the assumption of sphericity had been violated (W = .810,
χ2(2) = 11.820, p = .003), and so a Greenhouse-Geisser adjustment (ε = .840) was used for analysis.
A one-way repeated-measures ANOVA revealed a significant within-subjects effect of condition
2 on EMG OO (F(1.680, 95.775) = 22.007, p < .001, ηp = .279).
Post-hoc analysis revealed that for the main effect on transformed EMG OO, the Overload condition (M = -2.33, SD = .246) showed significantly higher levels of EMG OO than either the
Balance (M = -2.412, SD = .229, p = .001) or Boredom (M = -2.462, SD = .222, p < .001) conditions. Additionally, the Balance condition showed significantly higher levels of EMG OO than the Boredom condition (p = .005). See Figure 43 for a visualisation of these results.
Figure 48. EMG OO results (reversed).
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5.3.5 Results for the One-Way Repeated-Measures Multivariate Analysis of
Variance on Electromyography - Corrugator Supercilii
As with EMG OO, EMG CS was excluded from the RM MANOVA analysis of psychophysiological variables due to disparities in sample size. Therefore, in an effort to preserve statistical power for EDA, HR and HF, the data for EMG CS was assessed in a separate one-way repeated-measures ANOVA.
Mauchly’s Test indicated that the assumption of sphericity had been violated (W = .678,
χ2(2) = 12.817, p = .002), and so a Greenhouse-Geisser adjustment (ε = .757) was used for analysis.
A one-way repeated-measures ANOVA revealed no significant within-subjects effects of
2 condition on EMG CS (F(1.531, 51.443) = 2.492, p = .106, ηp = .068).
5.3.6 Results for Exploration of Predictive Relationships
To assess RQ2, a series of multiple regressions employing the psychophysiological measures as the predictors and each of the subjective player experience measure as the outcome measure (e.g., predicting the flow ratings from EDA, EMG OO, HR, HF and EEG) was undertaken. The initial set of regression analyses was conducted on data from the Balance condition, where evaluation of the subjective reports had established that the most optimal player experience was occurring. After assumption-checking, all regression equations were found to be non-significant (p > .05). Following this, the regression analysis was conducted using data for each participant drawn from the condition in which they experienced the highest levels of the outcome measure. For example, for the regression equation predicting interest/enjoyment, participants were filtered by highest rating of interest/enjoyment; predictions were then attempted on that value from the psychophysiological data obtained in the condition in which they
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experienced the greatest interest/enjoyment. Again, after assumption-checking, all regression analyses were found to be non-significant (p > .05).
To further analyse the data, correlations were calculated between the subjective and physiological measures. Very few significant correlations were found (see Table 7 for the Balance condition correlations). As a final further step to try to understand the cause of this lack of correlation, variances were calculated for each variable; the results show that many of the variables had low variance (see Table 8).
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Table 7. Correlations between subjective and psychophysiological measures
Flow Int/En Comp Auto Pres j EDA Pearson Correlation .053 .033 ‒.032 ‒.048 ‒.094 n = 81 Sig. (2-tailed) .635 .767 .774 .668 .403 EMG OO Pearson Correlation .110 .119 .223 .406 .299 n = 54 Sig. (2-tailed) .428 .392 .105 .002 .028 EMG CS Pearson Correlation .258 .253 .354 .055 ‒.011 n = 33 Sig. (2-tailed) .147 .155 .043 .761 .953 HR Pearson Correlation ‒.140 .035 ‒.205 ‒.026 -.006 n = 71 Sig. (2-tailed) .243 .774 .087 .828 .963 HF Peak Pearson Correlation ‒.052 ‒.059 ‒.139 .030 .099 n = 71 Sig. (2-tailed) .669 .627 .246 .807 .409 AF4 Alpha Pearson Correlation ‒.106 .071 ‒.014 ‒.022 .005 n = 60 Sig. (2-tailed) .420 .588 .916 .865 .971 AF4 Beta Pearson Correlation ‒.097 .004 .011 .036 .070 n = 58 Sig. (2-tailed) .469 .974 .932 .788 .603 AF4 Theta Pearson Correlation ‒.145 ‒.059 ‒.091 -.010 .013 n = 59 Sig. (2-tailed) .274 .657 .493 .940 .923 O2 Alpha Pearson Correlation ‒.115 .149 .039 .076 .093 n = 61 Sig. (2-tailed) .376 .251 .767 .560 .475 O2 Beta Pearson Correlation ‒.109 .122 .033 .087 .010 n = 59 Sig. (2-tailed) .413 .357 .807 .513 .943 O2 Theta Pearson Correlation ‒.138 .061 ‒.049 .018 .154 n = 60 Sig. (2-tailed) .291 .643 .709 .893 .241
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Table 8. Variances for all variables within conditions
Psychophysiological Boredom Balance Overload Measures EDA 45.571 47.154 46.485 ECG—HR 175.344 176.744 190.608 ECG—HF Peaks 0.006 0.007 0.005 EMG OO (below eye) 0.051 0.055 0.062 EMG CS (brow) - - - EEG AF4 Alpha 0.034 0.034 0.03 EEG AF4 Beta 0.04 0.038 0.037 EEG AF4 Theta 0.041 0.034 0.036 EEG O2 Alpha 0.037 0.04 0.037 EEG O2 Beta 0.057 0.058 0.053 EEG O2 Theta 0.031 0.03 0.033 Subjective Measures Boredom Balance Overload Flow 0.199 0.319 0.348 IMI Enjoyment 1.46 1.362 1.878 PENS Autonomy 2.064 1.754 1.64 PENS Competence 1.859 2.297 1.869 PENS Presence 1.497 1.779 1.611
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5.4 DISCUSSION
5.4.1 Subjective Experience of Play
The play conditions developed for this study follow precedents established in prior psychophysiological research in the player experience space (Nacke, 2008; Keller & Bless, 2008;
Keller et al., 2011). The conditions were designed to compare optimal and sub-optimal play experiences in a quantifiable way, as in research undertaken by Nacke et al. (2008), Keller and
Bless (2008) and Keller et al. (2011). To achieve this, the manipulation of challenge‒skill balance—an approach also employed by Keller and Bless—was selected for the condition design.
While challenge‒skill balance was originally selected for its integral role played in achieving flow
(Csikszentmihalyi, 1990), the manipulation remained relevant to the research program despite widening the focus to include additional psychological constructs. The use of three manipulations designed to support or inhibit the optimal play experience (challenge‒skill balance, challenge outstripping skill and skill outstripping challenge) provided a means of comparing psychological and psychophysiological responses.
5.4.2 IMI Interest/Enjoyment
As expected, results for the interest/enjoyment subscale of IMI showed significantly greater participant interest/enjoyment in the Balance condition than in the Boredom and Overload conditions. Separately, participants also reported greater interest/enjoyment in the Overload condition than in the Boredom condition. These results thus support the Balance condition—in relation to the Overload and Boredom conditions—as the optimal play experience in terms of interest/enjoyment. These findings also offer further evidence that challenge‒skill balance plays an important role in enabling a positive play experience, as supported by other research (Keller and Bless, 2008; Nacke and Lindley, 2008).
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Interestingly, the significant player preference for Overload over Boredom in terms of interest/enjoyment has implications for assessing play experience that contains challenge‒skill imbalances. This may tentatively point to challenge‒skill imbalance, wherein challenge outstrips skill, as a more enjoyable experience than the reverse—that is, overwhelming the player may lead to more positive experiences or enjoyment than underwhelming or boring the player. This is further strengthened by research undertaken by Abuhamdeh and Csikszentmihalyi (2009), in which levels of enjoyment among chess players were at their highest when players were up against better opponents, as opposed to those of equal ability; when perceived challenges were higher than player skill, the games were rated as more enjoyable. While these conclusions are not wholly applicable to the pattern of results in this study, in which the Balance condition was rated as more enjoyable than the Overload condition, this may assist in explaining the disparities between enjoyment of Boredom and Overload.
Another explanation may be that the presence of combat in a combat-based game, regardless of challenge‒skill balance or imbalance, is inherently more fun than or preferable to the complete absence of combat. As such, replication of this study with other game genres may be useful in future iterations of this work. However, when considering previous research that suggests a preference for low challenge among casual or inexperienced players (Alexander, Sear, &
Oikonomou, 2013; Lomas, Patel, Forlizzi, & Koedinger, 2013), it is important to consider that the findings of the present study may reflect the relatively high levels of self-reported experience among participants.
It is also possible that the Overload condition remained ‘fun’ for some individuals despite the excessive challenge of the condition. While the monotony of the Boredom condition ensured a consistent experience throughout play, the Overload condition may have allowed opportunities
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for moments of enjoyment—for example, when the participants were not yet aware of the condition’s impossibility, or as a consequence of any minor progress in the face of overwhelming odds. This was corroborated anecdotally through comments made by participants in a ‘General
Comments’ field in the respective condition surveys, including the following:
‘I had more determination to complete the objective even though I kept dying. Just getting
one or two cans was enough.’ (P210)
‘It had its moments, such as when everyone was away from me and I was healing up
myself, only at the VERY LAST MOMENT a zombie hitting me in the back—that was
kind of cool.’ (P113)
'I found it both more enjoyable and entertaining when being greatly challenged and failing
than when not challenged at all.’ (P132)
5.4.3 Flow
Flow findings generally followed the same pattern of results discovered in Chapter 4; while both Boredom and Balance induced significantly greater levels of flow than Overload, no differences between Boredom and Balance were revealed. This may point to the same possible conclusions established in the pilot study discussion:
that the immersive quality of highly detailed environments may still contribute to
experiences of flow, regardless of the challenge of the experience
that the condition design was unsuccessful in disinhibiting flow, or that challenge may
still be found in unexpected ways
that challenge‒skill balance may not be a strong antecedent or precursor to flow, despite
common conception
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or finally, that Jackson’s flow state scale (in this case, the S FSS-2) may not be applicable
as a measure of flow in play experiences.
As the original pilot study results directly informed the redesign of the Boredom condition, these conclusions may be expanded. Removing combat from the Boredom condition minimised the challenge of the game as much as possible while still retaining game-like elements
(primarily in the collection and retrieval of the gas canisters scattered throughout the map). It thus seems unlikely that any unexpected challenge was a consequence of limited or improper condition design. The success of the condition design in achieving boredom through challenge‒skill imbalance was corroborated anecdotally by feedback provided in the condition survey responses:
‘Seeing I was just walking around the game space and not really interacting that much
with the world made it feel really boring and time seemed to slow down going from each
objective.’ (P245)
‘Very few obstacles and dangers made the game a bit boring.’ (P142)
‘It felt very monotonous, with no combat or scares or music to really break up the flow of
the game and vary its pace.’ (P132)
Another possibility, as discussed in section 4.3.2, is that participants may have created their own challenges in lieu of challenge provided by the game. This is again reflected by participant comments provided in the survey sessions:
‘I set a challenge to myself to speedrun the game as quickly as possible (given my limited
skills).’ (P114)
‘I kinda made my own game out of juggling the canisters …’ (P113)
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It may be that flow is still possible in the absence of designed challenge (or in experiences in which the skills of the player outstrip the demands of the task, regardless of developer intention) due to the potential for player-created challenge. This may signal limitations in the consideration of flow as synonymous with optimal player experience; however, this finding also alludes to the implicit difficulty of manipulating challenge‒skill balance in an experimental setting. A player engaging with a boring or unchallenging video game at home has the option of simply discontinuing play; as a laboratory session presents inherent expectations or pressures to play the game, the player may instead contrive ways to make the experience more interesting—the experience would thus not reflect typical play. In the context of industrial research, this limitation could be avoided or minimised due to the accessibility of naturalised data from organic video game players. The potential effects of this, as well as possible strategies for managing them, are discussed in section 6.4. This may signal limitations in the consideration of flow as synonymous with optimal play experiences; however, these conclusions are not meaningfully supported by data evaluated within this program of research.
The lack of disparity in flow between the Balance and Boredom conditions despite the removal of combat in the latter condition also strengthens the remaining arguments discussed in section 4.3. The possibility remains that challenge‒skill balance may not be a necessary antecedent to flow, or to all experiences of flow, as supported by Fong et al. (2014) and Rheinberg et al.
(2003). Another explanation may be that the game’s immersive quality is enough for a flow experience, allowing for flow through absorption (Rheinberg et al., 2003). Finally, the S FSS and
FSS-2 scales may be inappropriate for assessing sub-optimal player experiences.
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5.4.4 Player Experience of Needs Satisfaction Competence
While participants reported significantly less competence in the Overload condition than both the Boredom and Balance conditions, no significant differences were found for competence between the Boredom and Balance conditions. The lower experience of competence in the
Overload condition again aligns with the condition’s design intentions; as the challenge was immediately overwhelming, and little progress could be made within the constraints of the condition, it follows that participants would experience less competence within this play experience.
The lack of significant differences between the Boredom and Balance conditions may be explained by the PENS competence items used to assess the experience. As may have also occurred with the FSS-2 and S FSS-2, some PENS competence items may be able to be rated highly for both challenge‒skill balance and skill > challenge imbalance. For example:
[Item 2] I felt very capable and effective when playing.
The absence of sufficient challenge in the Boredom condition removes the opportunity for failure or error. It may be that feelings of competence are thus not inhibited, allowing players to experience mastery over the condition’s simple fetch task. It is unlikely that the participants may have felt incapable, or ineffective, in the unimpeded retrieval of the gas canister objects.
Another item more directly addresses challenge‒skill balance, however, academic use of the commercial PENS scale does not extend to publishing this item. The item enquires about the player’s experience of challenge‒skill balance, but does not specify whether the absence of balance is the result of challenge exceeding skill or skill exceeding challenge. It is possible that participants responded to this item using either interpretation—that is, disagreeing with the item could indicate both ability exceeding challenge, or challenge exceeding ability. As also discussed
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in section 4.3.2, another possibility is that participants may have sought out their own challenge for this condition—for example, attempting to collect all the fuel canisters before the condition self-terminated.
Finally, experiences of competence in instances of sub-optimal or low challenge have been identified in previous PENS literature. Described as ‘mastery in action’, this experience occurs when players are granted the opportunity to ‘deliver a superlative performance without having to work too hard’ (Rigby & Ryan, 2007). Importantly, Rigby and Ryan note that this is not intended to represent the whole of the gameplay experience—rather, developers should provide opportunities for mastery in action as well as optimally challenging play. Thus, it is possible that the findings of the current study reflect participants’ experience of ‘mastery in action’ during the
Boredom condition, which resulted in high ratings of competence. However, this is arguably less likely given that the whole gameplay experience (in the Boredom condition) was low challenge, and ‘mastery in action’ is more likely to occur in situations where it is a ‘break’ from more optimally challenging play.
All potential explanations point to implications for designers and player experience researchers. Firstly, a high competence rating may not be indicative of challenge‒skill balance; researchers should consider that players may feel that their skills outstrip the challenge in a game, even when ratings suggest high levels of competence (unless the intention is to provide a ‘mastery in action’ experience). Secondly, should the players be setting their own challenge within an intentional skill > challenge imbalance condition, this highlights challenges associated with designing a boring play experience while keeping the game artefact ‘game-like’ (that is, not negating potential for invented challenge by removing interactive elements). Researchers and
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designers may wish to evaluate whether players are creating their own challenges within a post- game interview.
5.4.5 Player Experience of Needs Satisfaction Autonomy
Participants reported significantly greater autonomy in the Balance condition than in the
Boredom and Overload conditions, potentially positioning challenge‒skill balance as a precursor to autonomy (or challenge‒skill imbalance as an inhibitor of autonomy). No differences were found between the Overload and Boredom conditions. As a manipulation check, these results position Balance as the optimal play experience in terms of autonomy.
In the Boredom condition, the linear task and absence of combat may have contributed to reduced experience of autonomy: instead of being able to defeat enemy agents in interesting or entertaining ways, participants were relegated to the repetitive task of canister collection.
Likewise, in the Overload condition, participants were almost immediately overwhelmed by a large number of enemy agents, and thus constrained to the starting area of the map. Often, participant movement was restricted either through incapacitation or enemy AI body-blocking. As the enemy presence was continuous and overwhelming, it is also possible that the condition left no room for strategy or target prioritisation, thus forcing the player to simply ‘spray and pray’
(shoot randomly at enemies).
It is notable that the Boredom condition did not elicit greater autonomy than the Overload condition, as the Boredom condition provided participants with the ability to freely explore the game world, choose their own routes and not remain near their AI teammates for safety. It is possible that the presence of a clear challenge, even one that was overwhelming for the participant, was able to balance this. Alternatively, the absence of an achievable goal in both the Boredom and
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Overload conditions may have prompted participants to feel as though there were no meaningful choices to be made.
As increases in autonomy are positively associated with increased intrinsic motivation
(Deci & Ryan, 2000), the presence of greater autonomy in the Balance condition may indicate greater willingness to engage in the activity (Ryan et al., 2006). Through enabling a sense of freedom and choice for players, the Balance condition was able to strengthen its position as the optimal play experience of the three conditions.
5.4.6 Player Experience of Needs Satisfaction Presence
Finally, participants reported significantly greater presence in the Balance condition than the Boredom and Overload conditions, with no significant differences revealed between the
Boredom and Overload conditions. As presence has been identified as a key factor in the enjoyment of games (Lombard & Ditton, 1997), this again supports the Balance condition as the most reflective of the optimal play experience among the three conditions.
These findings also position challenge‒skill balance as either a possible antecedent to the experience of presence, or a strong predictor of it; conversely, this may also position challenge‒ skill imbalance as an inhibitor of presence, as supported by other research (Ravaja et al., 2004). It is possible that the experience of presence was obstructed or interrupted within the Overload condition due to unrealistic or notably skewed gameplay, thus taking players ‘out of’ the game world. This may have occurred as a consequence of detachment from gameplay once participants realised the insurmountable challenge of the game, thus diminishing mental or emotional investment in the activity. The high rate of player deaths may have also contributed to the interruption of presence, particularly as incapacitation and death removed participant control or input for up to 15 seconds; participants were then returned to the starting area, where the process
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began anew. In the event of repeated instances of player deaths, as occurred in the Overload condition, participants may not have experienced uninterrupted play for long enough to evoke experiences of presence.
Similarly, the complete absence of zombies or combat in the Boredom condition is at odds with the story of the game world (as told by NPC commentary, world assets and prior knowledge of the game), again potentially breaking the illusion of being transported to ‘within’ the game. It is also possible that a lack of mental stimulation in the Boredom condition prompted disengagement from the game, again inhibiting the potential for experiencing presence. These findings are also supported by research undertaken by Ravaja et al. (2004), which identified ‘easy’ game modes as less likely to invoke feelings of presence. When designing for presence in the game world, this points to the importance of obtaining challenge‒skill balance.
5.4.7 Confirmation of Condition Design Success
Generally, the challenge‒skill manipulation for the play conditions resulted in the emergence of clear optimal and sub-optimal play experiences. Aligning with design intention, the
Balance condition was the most successful of the three conditions in evoking psychological constructs that align with positive, or optimal, play experiences. The Balance condition was self- reported by participants as the most successful in terms of attaining presence, autonomy and interest/enjoyment. As for competence and flow, while no differences were revealed between the
Boredom and Balance conditions (potentially as a consequence of scale limitations), Balance still emerged as more successful than Overload.
No results revealed the Boredom and Overload conditions as more successful than the
Balance condition in eliciting an optimal play experience, allowing for a direct comparison of optimal (Balance) and sub-optimal (Boredom and Overload) conditions within the
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psychophysiological results. Differences were found between the Boredom and Overload conditions in some measures, with Boredom evoking greater experiences of competence and flow than Overload, and Overload evoking greater interest/enjoyment than Boredom. These subjective differences also assist in an exploration of the psychophysiological response to optimal and sub- optimal conditions that differ between challenge‒skill balance and imbalance.
5.5 PSYCHOPHYSIOLOGICAL RESPONSE TO PLAY
The following sections explore RQs 1a and 1b by investigating the psychophysiological response to the video game conditions. These also inform the overarching question of the utility of psychophysiological measures in evaluating play, thus exploring the research aim by expanding contemporary understanding of the value of psychophysiological measures in assessing the player experience. While this section primarily examines the differences that emerged in the psychophysiological response between the optimal and sub-optimal conditions, the potential for psychophysiological measures as a predictor of the subjective experience—and potential limitations in this approach—is also discussed.
5.5.1 Electrodermal Activity
Analysis revealed significantly lower levels of EDA in the Boredom condition than in the
Balance and Overload conditions, with no significant differences found between Balance and
Overload. The comparatively low levels of EDA in the Boredom condition may be unsurprising in the context of the game condition—whereas both Balance and Overload featured combat, violence, risk of failure and challenge, the monotonous nature of the Boredom condition entailed only that participants traverse an enemy-free map to fulfil a repetitive fetch task. Dawson et al.
(2000) report that EDA, primarily a measure of arousal, is associated with stimulus novelty, intensity, surprise, significance and emotional contentment; as most of these experiences are
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notably absent in the Boredom condition, this plausibly explains the lowered EDA response for the condition.
Despite the apparent intuitiveness of the result, the finding of decreased EDA in low- stress, boring or unchallenging play experiences is not corroborated by research in the player experience space. In research undertaken by Mandryk et al. (2006b) comparing beginner, easy, medium and hard difficulties, no main effects of difficulty level were found on any of the physiological measures, including EDA, employed within the study. Mandryk et al. suggest that this may be a consequence of inconsistent participant responses to the difficulty settings, and—as a result of methodology that featured consistent participant‒researcher interviews throughout— response to the experimental situation, rather than to the experimental manipulations. It should be noted that Mandryk et al. also report that only the beginner condition was perceived as significantly less challenging than the remaining conditions; this may indicate a lack of clear challenge distinction between conditions, or be a function of the relatively small sample size of seven.
The findings of this research program also differ from those of Kneer et al. (2016), in which no effect on physiological arousal (measured by EDA) as a consequence of difficulty was found. It is possible that the difficulty manipulations used in this study are incomparable to those used by Kneer et al.—for example, the Overload condition in this research was developed with the intention of overwhelming the player and rendering the task impossible; Kneer et al. aimed for higher difficulty, but possibly not to the same extent. This could also be true of the Boredom condition in comparison to the low-difficulty condition employed by Kneer et al. Another explanation for the disparity in results may be differences in experimental design—play times in the study undertaken by Kneer et al. were 20 minutes in length, compared with the 10-minute play
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sessions employed in this study. Therefore, it is possible that participants in Kneer et al.’s study played long enough to habituate more readily to the high difficulty than in the Overload condition used in this research.
Additionally, in Drachen et al.’s (2010) exploration of the physiological correlates of player experiences in first-person shooter games, no significant correlation between EDA and challenge—as measured by a self-report GEQ—was revealed. Drachen et al. propose that this was a function of imprecise wording of the scale used, emphasising the difficulties of evaluating challenge in player experience evaluation.
In Nacke and Lindley’s (2008) investigation of boredom, flow and immersion play conditions, the boredom condition generated greater psychophysiological activity—including
EDA—than the immersion condition. Additionally, greater EDA was revealed in the flow condition than in the boredom condition. These findings suggest that the relationship between difficulty and EDA response may be moderated by additional variables; whereas the conditions in the current research program varied only for challenge‒skill manipulation, Nacke and Lindley also manipulated audiovisual and sensory experiences (through narrative, sensory effects and environments). One conclusion could thus be that increased EDA indicates greater challenge, but not necessarily a more optimal player experience.
Further clarification may be found in research undertaken by Ravaja et al. (2008), in which increased EDA response was found when a player was killed or wounded, or when they killed or wounded an enemy (Ravaja et al., 2008). In the Boredom condition, no enemies were present and player death was impossible; in the Balance condition, some enemies were present, and player death was possible; and finally, in the Overload condition, an excessive number of enemies were present and player death was inevitable. Therefore, increased exposure to the death/wounding of
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both enemy opponents and the player‒character may also be partially responsible for increased
EDA response. This may also potentially be true of the results reported by Nacke and Lindley
(2008).
The applicability of Ravaja et al.’s (2008) conclusions to this study may be complicated by the absence of differences in EDA response between the Balance and Overload conditions. As previously stated, participants experienced greater enemy numbers and higher incidences of player death in the Overload condition than in the Balance condition; following these conclusions, an expectation may be that EDA response would be significantly higher for the Overload condition than the Balance condition. The absence of this difference, however, is possibly explained by the potential for detachment or disengagement from the activity, or by habituation.
Participants in the Overload condition may have disengaged, or ‘given up’, during play once the impossibility of completing the objective became apparent—as a result, the experience may have been less affecting in terms of physiological arousal. Therefore, the tonic exploration of the full 10-minute play session may present limitations in analysis, as participant arousal may have only diminished once they reached the conclusion of impossibility—thus resulting in electrodermal activation similar to those seen in the Balance condition. Anecdotal feedback participants provided during the survey sessions corroborate this experience of eventual detachment:
‘Having (what I assume) the difficulty turned up made it much more of a challenge, which
made me much more engaged with the task at hand, but as I kept dying, I started to become
less interested.’ (P245)
‘Once I realised the game was too hard I became more detatched [sic].’ (P243)
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‘Held attention till realisation of imposible difficulty at which point and want to achive
victory and progression is lost [sic].’ (P223)
As discussed in section 2.8.6.1, habituation describes the process wherein psychophysiological response is reduced after exposure to the continued presentation of, or interaction with, the same stimulus (Stern, 2001, p. 55). This may be explained by the repetitive experience of play in the Overload condition—players were almost overwhelmed by the enemy zombies, died and had to respawn and repeat the scenario until the session terminated. Conversely, in terms of study design, the semi-counterbalancing of the condition order may have resulted in habituation to the conditions regardless of differences in challenge. As the Overload condition was always placed last for fear of lasting effects on the Boredom and Balance condition, Overload may be more susceptible to habituation—a possible explanation for the absence of differences in EDA arousal between Balance and Overload.
A final explanation may simply be that challenge‒skill imbalance, wherein the challenge outstrips the skill of the player, is equally arousing within video game contexts as challenge‒skill balance. However, in the face of results gathered from other psychophysiological measures used within this study, this conclusion seems unlikely.
5.5.2 Electrocardiography—Heart Rate
Analysis revealed significantly greater HR in the Boredom condition than the Overload condition, with no significant differences revealed between the Boredom and Balance conditions.
The finding of greatest HR in the Boredom condition is seemingly at odds with the results gathered from EDA, as both are measures of arousal—in particular, as with EDA, HR has been found to increase during experiences of stress, mental activation and anxiety (Melillo, Bracale, & Pecchia,
2011; Allen et al., 1987; Szabo & Gauvin, 1992; Andreassi, 2007). However, as discussed in
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section 2.8.6.4, physiological arousal does not extend along a unidimensional continuum from low activation (deep sleep) to high activation (agitation, excitement or panic). As concluded by Lacey
(1967) and Stern et al. (2001), one form of arousal cannot always be used as a valid measure of another form of arousal, nor be used to singly represent the psychological experience of an activity.
The principle of stimulus-response specificity states that specific stimulus contexts may also bring about a specific pattern of physiological response, rather than a uniform increase or decrease across a unidimensional continuum. Furthermore, directional fractionation (Lacey, 1967) finds that physiological response does not decrease, or increase, uniformly across all measures. In
Stern et al.’s (2001) examples of both the missing wallet and the soldier on guard duty (discussed in section 2.8.6.4), EDA increases even as HR decreases. It could be that, in the context of video game play, play experiences may bring about a specific set of physiological response that features directional fractionation—in that while EDA decreases in instances of low challenge or boredom,
HR increases.
The increased HR in the Boredom condition may be an anticipatory response. Although
HR is an autonomic activity, the frequencies, variations and pace of the contractions are responsive to psychological stimuli of stressors, frustrations and fears (Andreassi, 2007, p. 438‒439); Melillo,
Bracale, & Pecchia, 2011). The increased HR may thus be a sympathetic response in preparation for combat or threats—the presence of which is suggested by not only the context of the chosen video game artefact (a well-known first-person shooter that typically features enemies combatants), but also the environments within the video game conditions (a post-apocalyptic world that contains multiple narrative and embedded suggestions of a zombie threat). This explanation is further supported by comments provided anecdotally in participant feedback for the
Boredom condition:
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‘There was still some excitement as I was half expecting zombies to turn up at any minute.’
(P140)
‘I think I learned quite quickly that the game was on it’s [sic] easiest mode and then began
to expect less shocks to almost not expecting any at all anymore towards the end.’ (P214)
‘I collected the can [sic], believing more enemies would spawn as you progressed
collecting the can …’ (P109)
Despite this, HR was lowered in the conditions that actually featured combat, threat and risk of failure. As with EDA, this may also be a consequence of stimulus-response specificity— in the context of the video game conditions, while HR increases in anticipation of a threat, actual combat may result in HR decreases.
Another explanation may be that, due to the relatively decreased experience of presence and interest/enjoyment (sections 5.4.6 and 5.4.2), participants were instead more conscious of their environment and thus responding instead to the experimental manipulations and setting—as experienced as a consequence of repeated interviews in research undertaken by Mandryk et al.
(2006b; discussed in section 2.9.1). Potential anxieties associated with the experimental setting
(unfamiliar laboratory environment, unfamiliar researcher and novel experiences) may thus have contributed to the HR increase that occurred within the Boredom condition.
A speculative explanation for the pattern of results may point to the lowered HR in
Overload and Balance conditions as a consequence of different experiences (as discussed in section 2.8.6.5, many-to-one and one-to-many domain relationships suggest that the same physiological response be indicative of separate—and occasionally contrasting—psychological experiences). Research undertaken by Nacke and Lindley (2008) revealed decreased physiological arousal in an immersion condition than in a boredom condition; as immersion is conflated with
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presence, the same may hold true in the context of HR results in this research program. If decreased psychophysiological arousal is potentially associated with immersive game worlds, it is plausible that the Balance condition evoked a lower HR as a consequence of its immersive environments.
Separately, participants playing the Overload condition may have also experienced a lower HR as a consequence of habituation, or alternatively, disengagement in the face of overwhelming challenge.
The discovery of a main effect of challenge‒skill balance on HR contrasts with research undertaken by Mandryk et al. (2006b) and Kneer et al. (2016), in which no main effect of difficulty on HR was discovered. Mandryk et al. suggest that an absence of results in their study may be the consequence of the experimental procedure; however, in terms of the research undertaken by
Kneer et al., the disparity may lie in differences in play session lengths and condition design (as discussed in section 2.9.2.1). Conversely, research undertaken by Drachen et al. (2010) revealed correlations between increased HR and feelings of frustration and tension reported during play of three first-person shooter games; this could support the potential for the physiological response to be driven by anticipation in the Boredom condition, and disengagement in the Overload condition.
5.5.3 Electrocardiography Heart Rate Variability (High-frequency Peaks)
Significantly lower HF peaks were found in the Overload condition than in the Balance and Boredom conditions, with no significant differences found between Balance and Boredom.
The lowered HF peaks in the Overload condition are congruent with psychophysiological literature: HF reflects parasympathetic activity and has been found to decrease under conditions of emotional strain and anxiety (Nickel & Nachreiner, 2003; Jönsson, 2007; Billman, 2013), with decreases in general HRV associated with increases in stress states (Schubert et al., 2009). It is
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thus reasonable to assume that the overwhelming difficulty of the Overload condition, coupled with continued failure to progress, was capable of inducing emotional strain and stress.
The absence of differences in HF peaks between the Balance and Boredom conditions are surprising due to the associations between decreases in the HF component of HRV and increased mental workload and attentional focus (Cinaz et al., 2010; Hjortskov, N., 2004). An interpretation for this may be that the Boredom condition, despite the monotony of its experience, evoked similar levels of attentional focus or mental workload as the Balance condition due to anticipatory response, as discussed in section 5.5.1. Additionally, as Balance and Boredom did not differ in terms of self-reported flow, mental workload may have been equally diminished (assuming these self-reported results represent actual experience): several of the flow dimensions describe a merging of action‒awareness, loss of self-consciousness and a sense of control over the activity.
However, this argument is weakened somewhat by the presence of an additional dimension concerning intense and focused concentration
These results fail to align with those of previous research undertaken by Keller et al.
(2011). In Keller et al.’s investigation of the psychophysiological response to flow as manipulated by challenge‒skill balance, decreases in HRV indicating enhanced mental workload were reported in a challenge‒skill balance (‘fit’) condition. Accordingly, Keller et al. posit that physiological elements that reflect tension and mental load may also indicate flow in the player experience.
However, it is critical to note that two substantial distinctions between the study and interpretation approach may account for the disparity in results. A careful reading of the study analysis shows that the difference between the fit and overload conditions was not significant, with the p-value (p
< .10) interpreted by Keller et al. as ‘trend-level significance’; in this program of research, however, differences not achieving significance are not interpreted. An additional key difference
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is found in the choice of the game artefact for study: Keller et al. employed a game based on the television show Who Wants to be a Millionaire, featuring quiz-format questions participants were required to answer from a multiple-choice selection within a limited time period. As discussed in section 2.9.6, the generalisability of this artefact within the space of player experience is potentially limited. Furthermore, the quiz-like nature of the chosen game artefact is likely to differ in terms of mental workload from that experienced in the action first-person shooter selected for this research program; arguably, in the video game condition selected by Keller et al., explicitly requiring increased mental workload was the game’s primary function.
5.5.4 Electromyography Orbicularis Oculi
Analysis revealed that the Overload condition featured significantly higher levels of EMG
OO activity than either the Balance or Boredom conditions. Additionally, the Balance condition showed significantly higher levels of EMG OO activity than the Boredom condition. As EMG OO is primarily employed as a measure of positively valenced emotion through activation of the OO muscle that occurs when smiling (Andreassi, 2007, pp. 300–303), these results present interesting implications for the role of EMG OO in evaluating player experience.
The most intuitive result could be the relative lack of EMG OO activity in the Boredom condition when compared with the Balance and Overload conditions. Again, the monotony of the
Boredom condition is potentially a root cause for the reduced activity; as the only task available to the players in this session was the repetitive retrieval of gas canisters, there was a notable absence of events capable of provoking an emotional experience positive enough to elicit a smiling response. This finding is supported by research undertaken by Nacke and Lindley (2008), in which the flow condition—featuring challenge‒skill balance—prompted greater EMG OO activity than the boredom condition. Notably, in an exploration of physiological associations with flow
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experiences, Kivikangas (2006) was unable to find associations between flow and EMG OO; while the current research program is unable to be directly compared with specific investigations of flow experiences, the EMG OO results reported herein may further expand on the current understanding of physiological response to optimal play conditions.
Of particular interest is the greater activity of EMG OO in the Overload condition than the Boredom condition. It is feasible that, owing partially to the immersive nature of the Balance condition and the interrupted nature of the Overload condition, participants were more expressively responsive in the Overload condition. To extrapolate on this, player deaths within the game world allowed for up to 30-second ‘breaks’ from play that were not also present in the
Boredom and Balance conditions. During these breaks, participants could emote in response to the level’s overwhelming difficulty or the participant player‒character’s death in the game; these expressions could have been the result of exasperated, nervous or amused laughter, either as a consequence of stress relief or pleasure, the presence of which is anecdotally supported by feedback provided in the survey session for the Overload condition:
‘[I] couldn't help but laugh before and during the game at the difficulty.’ (P130)
In addition to the above participant comment, this interpretation is further supported by researcher observation of the participants during the study.
Another possible explanation for the Overload results may stem from the tonic analysis limitations. As the phasic assessment of EMG is not reliably feasible within commercial contexts due to the associated time costs, and tonic analysis of EMG has been previously employed in player experience literature (Kivikangas, 2006; Nacke & Lindley, 2008), EMG OO was assessed tonically within this program of research. However, it should be noted that EMG OO is typically assessed within psychophysiological spaces through the evaluation of phasic reactions to a single
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stimuli (Ekman, Davidson, & Friesen, 1990), and is an intended avenue of exploration in future research.
A disadvantage of tonic analysis is the potential for conflating startle response with psychophysiological results (see section 2.8.6.3). The magnitude of startle response—defensive eye-blink reflexes to unexpected or threatening stimuli, capable of being recorded as EMG OO activity—is inversely related to the valence of the stimuli (Lang, 1995). In the experience of the
Overload condition, it is plausible that the high presence of threat, tendency for enemy combatants to attack from behind and sudden deaths may have resulted in higher incidences of startle response than either the Boredom or Balance conditions. Therefore, despite the interpretation of EMG OO as a measure of positive valence, it is possible that the OO activity is also conflated with experiences of negative valence through startle response. In their exploration of the psychophysiological experience of flow, Kivikangas (2006) identifies this as a possible limitation for interpreting their results.
Finally, player experience of the Balance condition is associated with higher levels of
EMG OO than Boredom and lower levels of EMG OO than Overload. If the EMG OO activity of
Overload is not conflated with startle response, this suggests that researchers and developers evaluating player experience should be cautious of interpreting high OO activity as necessarily indicative of positive or optimal experiences. These implications for using EMG OO in the assessment of the player experience are further discussed in section 6.5.
5.5.5 Electromyography Corrugator Supercilii
No significant results were obtained in the analysis of EMG CS. This is likely a consequence of the notably reduced n available for evaluating this measure, as a probable
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consequence of signal loss or unresolvable noise levels (see section 5.1.16). Recommendations for future methodologies employing this measure are suggested in section 6.4.3.
5.5.6 Electroencephalography
A similar pattern of results was found for all measures of EEG that achieved significance: in the evaluation of the absolute power of AF4 Beta, O2 Alpha, O2 Beta and O2 Theta, activity in the Balance condition was found to be significantly lower than activity in the Boredom condition.
In all instances, no differences were revealed between Balance and Overload. This section discusses a potential cause for this similar pattern of results, and offers interpretations for the results as they occurred.
5.5.6.1 AF4 and O2 Beta
The most intuitively interpretable findings are the pattern of results revealed for the AF4
Beta and O2 Beta frequency bands. As beta activity occurs during states of alertness, it is most common when an individual is engaged in mental activity (Andreassi, 2007, p. 69), and is associated with cognitive task demands, information processing and problem-solving (Fernandez et al., 1995; Cole & Ray, 1985; Nacke, 2010).
In terms of the Boredom condition, beta activity reduction may occur as a consequence of reduced mental simulation through monotony and task repetition; the relatively simple fetch task—moving towards a highlighted object on the map, collecting it and returning it to the same designated location—does not represent a cognitively demanding task, nor allow room for problem-solving beyond determination of the shortest routes to the highlighted objects. Reduced
AF4 and O2 Beta in the Boredom condition can thus be explained by a dearth of cognitive stimulation.
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The Balance condition is intuitively facilitative of increased beta activity as a consequence of challenge‒skill balance. Unlike the Boredom condition, the Balance condition necessitates problem-solving and information processing in order to successfully complete the games objectives. The opportunities for problem-solving present in a multitude of ways: special enemies
(see section 3.3.4) require unique strategies to defeat; some enemies require immediate prioritisation over others due to proximity to the player‒character; the participant must decide whether they need to drop a canister to shoot the enemy, or attempt evasive action; managing health and ammunition resources is necessary; panic events—in which multiple enemies appear at once—require the player to play defensively; and so on. As the cognitive demands increase, mental activity, problem-solving and information processing also increase to meet the demands.
The increased beta activity in the Overload condition, with no differences revealed between Overload and Boredom, could suggest the same conclusions introduced in section 5.5.2: players may have disengaged at a certain point within play, but not immediately. Increased beta activity may thus occur in the Overload condition for the same reasons as the Balance condition
(problem-solving, cognitive demand and information processing). However, as physiological activation does not take place on a single unidimensional continuum, beta in the Overload condition failed to outstrip the Balance condition as a consequence of impaired engagement at some point during play.
Within the context of electroencephalographic studies of the player experience, beta activity has also been associated with feelings of spatial presence (Nacke, 2010; Johnson et al.,
2015). As the Balance condition was also found to score highest on PENS presence, this may represent an additional influence on the increased beta activity in Balance—further supporting
EEG Beta activity as indicative of video games presence. Despite this, a predictive or correlational
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relationship was not shown between PENS presence and EEG Beta, although this may be a consequence of variances; see section 5.3.6 for discussion on this.
5.5.6.2 O2 Theta
As revealed in section 2.8.7.4, the psychological stimuli responsible for theta activity is less clear-cut than beta. Theta has been found to occur in both states of drowsiness, sleep, problem- solving and attentional focus (Stern et al., 2001, p.81), as well as daydreaming, creativity, emotional processes and working memory tasks (Mitchell et al., 2008). Within player experience literature, relationships have also been established between theta activity and challenge or engagement (Salminen & Ravaja, 2007).
The obvious connection, and potentially the most reasonable explanation for these results, is that of increased theta activity with challenge and engagement. The play conditions were directly manipulated for challenge, such that the Balance condition emerged as the optimal play experience through challenge‒skill balance and Overload as sub-optimal through challenge > skill imbalance. The relatively decreased theta activity in the Boredom condition may again be explained by disengagement as a consequence of monotony, with absence of challenge in the
Boredom condition occurring due to the removal of combat. If these assumptions reflect the participant experiences, the increased theta activity in the Balance and Overload conditions aligns with extant player experience literature.
Other explanations for the increased theta activity in Balance and Overload emerge through theta associations with creativity, emotional processing, attentional focus and working memory tasks. A state of creativity and attentional focus may occur as a consequence of strategy and problem-solving; furthermore, as the Balance condition allows for more ‘highs and lows’— rather than being consistently monotonous or consistently overwhelming to the point of
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disengagement—emotional processing may also be at its peak in the Balance condition. Finally, working memory may occur as a consequence of remembering how to best approach the special zombie types, as each zombie has its own unique attack and strategy; these solutions are unserviceable in the Boredom condition due to the absence of enemy units.
Overall, these conclusions suggest that theta activity in video game play is unlikely to indicate the restful states also associated with theta activity. This may be because, regardless of the level of game challenge, simply playing a video game and acting within game environments could inhibit theta activity associated with drowsiness.
5.5.6.3 O2 Alpha
Alpha activity (EEG in the 8-12 Hz range) is observed in individuals when they are awake but relaxed, a state of ‘relaxed wakefulness’ (Davidson et al., 2000, p. 31), and is particularly associated with the closing of the eyes. Alpha activity is associated with a relative lack of cognitive processing (Stern et al., 2001, p. 80), with the presence of alpha indicative of ‘idling’ (Schier,
1999). Increases in alpha activity are typically interpreted as indicative of less attentional activity, with decreases indicative of more attentional capacity. This is further emphasised by the Berger effect, wherein the amplitude of the alpha band decreases notably when the subject opens their eyes (Bazanova & Vernon, 2014). Complex cognitive tasks, or the introduction of a stressor, interrupt increased alpha states in a process known as ‘alpha blocking’ (Stern et al., 2001, p. 80).
Within player experience research, alpha has been found in at least one paper to decrease with positive mood (Russoniello et al., 2009).
The increased alpha observed in the Overload condition is potentially attributable to disengagement or habituation due to the overwhelming task demands. This disengagement may have resulted in a relaxed state, or less attentional activity, increasing the presence of theta activity.
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This could also explain why increased alpha has emerged alongside increased beta and theta activity, as these mental states may have occurred at different stages of play within the Overload condition. Additionally, if the relationship observed by Russoniello et al. (2009) hold true, increases in negative mood as a consequence of repeated failure—thus increasing frustration or anxiety—may also be responsible for increases in alpha activity in the Overload condition.
That alpha activity was significantly greater in the Balance condition than in the Boredom condition is surprising. It was expected that increased alpha, indicative of increased relaxation, might be associated with the Boredom condition. One explanation may be that, as the Balance condition featured combat and risk of failure, more emotional ‘highs and lows’ occurred than in the Boredom condition; as such, O2 Alpha may have increased with negative mood. Jennet et al.
(2008) hypothesises that faster paced games also lead to greater negative affect than do slower paced games, further supporting the notion that the Balance and Overload condition evoked greater negative emotional experiences than did the Boredom condition. Despite this, increased alpha activity is typically associated with less attentional activity (Schier, 2000); it is possible that, in the specific context of gameplay, alpha activity may be more indicative of negative emotional experiences than of attentional inactivity. As such, future research may expect to find increased alpha activity in more emotionally or cognitively challenging play experiences.
Another explanation may be that increased alpha in the Balance condition represents a state of ‘peak flow’, or relaxed—but pleasurable—engagement. This doesn’t aid in understanding of the increased alpha results in the Overload condition, but (as established in section 2.8.6.5) the same physiological response is not necessarily indicative of the same psychological processes.
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Despite these possible explanations, these findings remain perplexing. The large effect sizes associated with the EEG results suggest that this is an effect to pursue in future research— especially if the counter-intuitive results are indicative of optimal states.
5.5.6.4 Shared Pattern of Results
One potential explanation for the shared pattern of results among all frequency bands and both sites may be the limitations of the equipment used for recording. The EEG instrument used for this study, a 14-sensor dry array EMOTIV Epoc EEG headset, was primarily designed for
‘practical’ research in commercial and research spaces. In Duvinage et al.’s (2013) comparative analysis of the EMOTIV Epoc EEG headset with a medical grade counterpart, the Epoc headset was found to be limited in its performance as a consequence of inconsistent positioning, greater potential for movement artefacts and a greater chance for misinterpretation of cognitive activity.
Despite this, Duvinage et al. state that the EMOTIV Epoc headset is ‘not bad at all for such a low- cost system’, with responsiveness to participant cognition far above the chance level of 25%.
Furthermore, Duvinage et al. recommend the headset for use in non-critical contexts, such as games. It should be highlighted here that this analysis employed the EMOTIV Epoc as an instrument for interaction between the brain and computer, and not for frequency analysis as undertaken within this program of research.
As a potential consequence of these issues, the similar pattern of results may point to issues in evaluating the absolute power of individual frequency bands with the chosen analysis method or equipment—results could thus simply be interpreted as general cognitive activity. This may indicate that Boredom was less cognitively demanding overall, as a consequence of the monotony, repetition and ease of the condition. Furthermore, the absence of differences between
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the Balance and Overload conditions could indicate similar levels of cognitive activity or disengagement at some point during play of the Overload condition.
5.6 EFFECT SIZES
A low- to medium-effect size was revealed for many of the psychophysiological measures; in particular, EDA, HF and HR revealed effect sizes between .072 and .102 (see Table
9 for all effect sizes). However, more relevant for this research program is the comparison of effect sizes, which allows for the discernment of useful psychophysiological measures to explore optimal and sub-optimal conditions (particularly those that vary for challenge‒skill balance).
Table 9. Effect sizes for psychophysiological measures
2 EDA ηp = .081 2 HR ηp = .071 2 HF Peak ηp = .102 2 EMG OO ηp = .279 2 EEG AF4 Beta ηp = .137 2 EEG O2 Alpha ηp = .336 2 EEG O2 Beta ηp = .389 2 EEG O2 Theta ηp = .366
Overall, EMG OO and the EEG O2 site emerged as having the largest effect size for differences between play conditions. This situates both measures as the most effective, among the psychophysiological measures used within this research program, for detecting differences between optimal and sub-optimal play experiences. Interestingly, EMG OO and the O2 site offer effect sizes comparable to, and greater than, effect sizes reported for subjective evaluation (see
Table 10).
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Table 10. Effect sizes for all subjective measures
2 Flow ηp = .282 2 Interest/Enjoyment ηp = .277 2 Competence ηp = .492 2 Autonomy ηp = .237 2 Presence ηp = .251
With the exception of competence, EEG O2 appears more effective at detecting differences between the play conditions than subjective analysis. Additionally, with the exception of competence and flow, EMG OO also emerges as a more robust tool for determining experience differences between conditions.
The magnitude of the differences revealed by EEG OO is potentially a consequence of
O2’s proximity to the occipital lobe. Many regions of the occipital lobe are specialised for tasks related to visuospatial processing, motion perception and receiving visual information. The presence of enemy combatants in the Overload and Balance conditions may thus explain the significantly increased activity in the O2 region for these conditions.
Furthermore, the comparatively strong effect size found for EMG OO provides additional support for a relationship between increased challenge and increased EMG OO activity; this finding depicts EMG OO as a particularly robust approach for evaluating differences between low challenge and high-challenge player experiences.
The low effect sizes revealed for the remaining psychophysiological measures do not lessen the importance of the results. As psychophysiological assessment is constrained to operate within parameters normal for physiology, small effect sizes may not be indicative of weak results.
However, these findings do potentially indicate that EDA, HF Peaks, HR and the AF4 site are less capable of discerning granular differences between conditions than EEG O2 and EMG OO in the
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specific context of challenge manipulation; it should be noted, however, that these results may differ in the manipulation of different stimuli (for example, violence, sound, or graphical representations).
6 SUMMARY AND CONCLUSIONS
This program of research has explored the psychophysiology of the player experience, as well as the applicability of the psychophysiological method to player experience evaluation, through a large-scale approach incorporating a large sample size and the contemporaneous employment of multiple physiological measures. Both subjective and psychophysiological measures were assessed separately. The pattern of results for the subjective measures confirmed the successful creation of optimal and sub-optimal experiences, and differences emerged between play conditions for EDA, EMG OO, ECG (HR), ECG (HF peaks), AF4 Beta, O2 Alpha, O2 Beta and O2 Theta.
6.1 SELF-REPORT SUMMARY
In terms of subjective experience, the play conditions were found to largely follow the expected pattern of results, with Balance emerging as the most positively received play condition.
While this largely confirmed the success of the play conditions in evoking optimal and sub-optimal play experiences, some surprising results became apparent that suggest both the difficulties of player experience analysis and unexpected player preferences. These results expand on existing knowledge of the player experience and player experience evaluation methods. See Table 11 for an overview of the results for the self-report measures.
In all self-reported measures, Balance emerged as the most positively received condition.
For the flow and competence measures, the Boredom condition was received as positively as the
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Balance condition, but Balance was at no point outstripped by either the Boredom or Overload conditions. Overall, this establishes the Balance condition as the optimal play experience and confirms the success of the condition design. This finding highlights the importance of challenge‒ skill balance in ensuring an optimal or positive play experience, as supported by a meta-analysis of literature featuring challenge‒skill manipulations undertaken by Fong et al. (2014). However, it is critical to note that this conclusion may be influenced by the expertise of the sample, with participants self-rating as 5.96 out of 7 for ‘general experience with video games’; in previous research, the role of challenge and challenge‒skill balance has been theorised as less important for novice players (Lomas et al., 2013; Alexander et al., 2013).
As discussed in sections 5.4.3 and 5.4.4, the high ratings for flow and competence in the
Boredom condition may indicate complexity in the evaluation of easy, or skill > challenge imbalanced, player experiences. Two theories were posited for this: either the scales for assessing flow and competence did not allow for the distinction between challenge‒skill balance and skill > challenge balance experiences, or the potential for player-created ‘fun’ or challenge must be considered in future evaluation of player experiences.
A final interesting note is the performance of the Overload condition in terms of interest/enjoyment. The Overload condition was significantly more positively received than the
Boredom condition, potentially indicating player preference for challenge > skill imbalance over skill > challenge imbalance. As with the reception of challenge‒skill balance, however, this may be influenced by the expertise of the sample collected for this program of research.
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Table 11. Overview of significant results for subjective measures
2 S FSS - Flow (ηp = .282) Boredom Balance Overload Boredom ‒ BO > OL** Balance ‒ BA > OL** Overload BO > OL** BA > OL** -
2 IMI: Interest/Enjoyment (ηp = .277) Boredom Balance Overload Boredom ‒ BA > BO** OL > BO** Balance BA > BO** ‒ BA > OL** Overload OL > BO** BA > OL** -
2 PENS: Competence (ηp = .492) Boredom Balance Overload Boredom ‒ Balance BA > OL** ‒ BA > OL** Overload BO > OL** BA > OL** -
2 PENS: Presence (ηp = .251) Boredom Balance Overload Boredom ‒ BA > BO** Balance BA > BO** ‒ BA > OL** Overload BA > OL** -
2 PENS: Autonomy (ηp = .237) Boredom Balance Overload Boredom ‒ BA > BO** Balance BA > BO** ‒ BA > OL** Overload BA > OL** - * p < .05, ** p < .01 Boredom = BO Balance = BA Overload = OL
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6.2 PSYCHOPHYSIOLOGY SUMMARY
Analysis of the physiological response to the condition revealed results both congruent and incongruent with existing literature within the player experience domain (see Table 12 for an overview of these results). Potential explanations for this complicated pattern of results, rooted in both psychophysiological and player experience research, are offered in more comprehensive detail in section 5.5. Overall, these results contribute to current understanding of the psychophysiological experience of play and offer insight into psychophysiological methodologies in player experience evaluation contexts.
Measures of physiological arousal revealed interesting, novel and occasionally incongruent results. The Overload condition featured significantly increased EDA and lower HF peaks than the Boredom and Balance condition; this contrasts with previous psychophysiological literature in the player experience space, in which no main effect of difficulty on either of these measures was found (Mandryk et al., 2006b; Keller et al., 2011; Kneer et al., 2016). This research program reveals that EDA and HF response to game difficulty can emerge, corresponding with domain-level psychophysiological literature associating these physiological responses with stress, tension and anxiety (Dawson et al., 2000; Nickel & Nachreiner, 2003; Jönsson, 2007).
Despite this, the Boredom condition featured a significantly higher HR than the Overload condition—as increased HR is also associated with high arousal, stress and emotional intensity
(Andreassi, 2007; Melillo et al., 2011), this result seemingly contradicts the pattern of EDA and
HF results revealed in the Overload condition. Furthermore, the incongruity between lower peak
HF in the Overload condition and increased HR in the Boredom condition is surprising. A timebin analysis of the first and last five-minute windows of both the Boredom and Overload conditions would yield a richer understanding of this incongruity; this is further discussed in section 6.5. An
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additional explanation, as offered in section 5.5.1, may be that habituation or disengagement in the Overload condition could specifically explain relatively higher HR in the Boredom condition.
The disparity might also be explained more generally by stimulus-response specificity, in that physiological response does not increase uniformly across all measures.
The EMG OO findings somewhat corroborate player experience research undertaken by
Nacke and Lindley (2008), in that OO was lowest in the boredom condition employed in their research. Generally, the findings reported in this research program may imply that challenging player experiences are more capable of inducing OO activity than unchallenging or easy player experiences. This develops current understanding of tonic assessment of EMG in player experience literature.
The findings for the EEG AF4 and O2 Beta frequency bands may suggest decreased mental stimulation in the Boredom condition as a consequence of monotonous or repetitive experiences; furthermore, mental stimulation, information processing and problem-solving are associated with the increased challenge experiences of the Balance and Overload conditions. As presence has been associated with beta activity in previous player experience research (Nacke,
2010), this may also explain the increased beta activity in Balance (rated highest on PENS presence). The increased O2 Theta in Balance and Overload supports previous research undertaken by Salminen and Ravaja (2007) in which theta was associated with game challenge; alternatively, theta associations with creativity, emotional processing, attentional focus and working memory tasks may also explain these results. The conclusions also support the notion that theta activity in video game play is unlikely to indicate the restful states also associated with theta activity. Finally, the findings for increased O2 Alpha in Balance and Overload may be attributable to the improved potential for emotional ‘highs and lows’, otherwise inhibited by the
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monotony and task repetition of the Boredom condition. Disengagement or habituation throughout the Overload condition may also explain the increased alpha activity.
Despite these findings, one explanation for the EEG findings—as a consequence of the shared pattern of results among all frequency bands and sites—is that more cognitive activity occurred during the Balance and Overload conditions. However, interpreting the individual frequency bands prompts potential useful insights into the psychophysiological response to play.
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Table 12. Overview of significant results for psychophysiological measures
2 EDA (ηp = .080) Boredom Balance Overload Boredom ‒ OL > BO** Balance ‒ Overload OL > BO** ‒ 2 HR (ηp = .072) Boredom Balance Overload Boredom ‒ BO > OL** Balance ‒ Overload BO > OL** ‒ 2 HF Peak (ηp = .102) Boredom Balance Overload Boredom ‒ BO > OL** Balance ‒ BA > OL* Overload BO > OL** BA > OL* ‒ 2 EMG OO (ηp = .279) Boredom Balance Overload Boredom ‒ BA > BO** OL > BO** Balance BA > BO** ‒ OL > BA** Overload OL > BO** OL > BA** ‒ 2 EEG AF4 Beta (ηp = .136) Boredom Balance Overload Boredom ‒ BA > BO** OL > BO* Balance BA > BO** ‒ Overload OL > BO* ‒ 2 EEG O2 Alpha (ηp = .335) Boredom Balance Overload Boredom ‒ BA > BO** OL > BO** Balance BA > BO** ‒ Overload OL > BO** ‒ 2 EEG O2 Beta (ηp = .389) Boredom Balance Overload Boredom ‒ BA > BO** OL > BO** Balance BA > BO** ‒ Overload OL > BO** ‒ 2 EEG O2 Theta (ηp = .368) Boredom Balance Overload Boredom ‒ BA > BO** OL > BO** Balance BA > BO** ‒ Overload OL > BO** ‒ * p < .05, ** p < .01 Boredom = BO Balance = BA Overload = OL
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6.3 EXPLORATION OF RESEARCH QUESTIONS AND AIM
This program of research sought to identify the effectiveness of psychophysiological measurement in player experience evaluation. To explore this, a large-scale psychophysiological study was undertaken to both investigate the utility of various physiological measures and assess the psychophysiological response to optimal and sub-optimal player experiences. This section explores the research questions and aim in the context of the results discussed in this chapter.
Research Questions
1. How effectively can psychophysiological measures be used to evaluate the player
experience?
a. What are the differences in psychophysiological response between optimal and
sub-optimal play experiences?
b. Which psychophysiological measures, or combination of psychophysiological
measures, most reliably predict specific components of the player experience
as assessed by subjective measures?
Initial analysis confirmed the successful creation of an optimal and sub-optimal play condition, allowing for the exploration of RQ1a, although some results indicate challenges associated with evaluating non-optimal player experiences. In the psychophysiological assessment of the differences in psychophysiological response between optimal and sub-optimal play experiences, several differences were revealed (for an overview, please see Table 12): the Overload (sub- optimal in that challenge > skill) evoked greater EDA than the Boredom condition (sub-optimal in that skill > challenge), greater EMG OO activity than both the Boredom and Balance conditions and greater AF4 Beta, O2 Alpha, O2 Beta and O2 Theta than the Boredom condition. The Balance condition (challenge‒skill balance; optimal condition) evoked higher HF peaks than the Overload
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condition, and greater EEG AF4 Beta, O2 Alpha, O2 Beta and O2 Theta than both the Overload and Boredom conditions. Finally, the Boredom condition evoked a higher HR than the Overload condition and higher HF peaks than the Overload condition. Interpretations of these results, and what they may reflect about the player experience, are discussed in section 5.5.
RQ1b required the assessment of predictive relationships, employing the psychophysiological measures as the predictors and specific components of the player experience
(the subjective measures) as the outcome. As multiple regressions analysis revealed no significant regressions equations, and correlations testing very few significant correlations, no psychophysiological measures were found to reliably predict specific components of the player experience. The reasons for this are offered in section 5.3.6, but broadly, this may establish psychophysiological measures—particularly when restricted to a feasibly obtainable sample size—as limited in the granular prediction of specific psychological concepts such as flow, interest/enjoyment, presence, competence and autonomy within the context of challenge-skill balance manipulation.
Aim: To further clarify existing contributions to literature by expanding understanding of the psychophysiological experience of play, and the value of psychophysiological measures as a means of assessing the player experience.
Ultimately, this program of research has addressed the aim of expanding current understanding of the value of psychophysiological measures in assessing the player experience.
The psychophysiological responses to optimal and sub-optimal player experiences revealed within this research program has expanded upon extant psychophysiological player experience literature; in particular, it has revealed new findings regarding the physiological effect of challenge.
Psychophysiological assessment has been supported by this research as a useful and insightful
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approach for player experience evaluation, and particular recommendation is made for its employment alongside subjective analysis. The findings of this research program also contribute methodological recommendations for using psychophysiological analysis within player experience evaluation contexts.
6.4 APPLICABILITY OF PSYCHOPHYSIOLOGICAL ASSESSMENT
One aim of this research program was to assess the utility of psychophysiological measures and methodologies in their application to player experience evaluation in both academic and commercial contexts. As this research represents one of the first large-scale psychophysiological evaluations of the player experience, this section largely discusses this applicability in the context of the data collection, treatment and analysis of Study 2. This section is additionally informed by the research program limitations, and existing psychophysiological research in player experience. Recommendations are made for psychophysiological evaluation in future research.
6.4.1 Time Costs
A notable disadvantage of psychophysiological assessment is the temporal cost associated with experimental set-up, data treatment and data analysis. This program of research already sought to minimise time requirements by employing tonic, as opposed to phasic, analysis of every psychophysiological measure (including contexts where phasic assessment is typically used; see section 5.5.4). Despite this, time costs associated with even tonic evaluation may prove prohibitive within academic and commercial player experience research contexts. However, there are solutions available to further minimise time requirements.
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The contemporaneous use of EDA, EMG OO, EMG CS, EEG and ECG markedly affected the experiment runtime, adding a total of 40 minutes (of mandatory participant presence) to the experiment set-up. As participants cannot freely move during set-up or data collection, the entire experimental process required that participants remain seated for a total of two hours—an experience that some described as ‘uncomfortable’. The maintenance of EL254 EMG and ECG electrodes further contributed to the time costs of each experiment session, as the electrodes required scrubbing in tepid water and a five-minute exposure to disinfectant to both sterilise them and prevent the potential for remaining electrode gel to dry within the electrode cup cavity.
Considerable time costs are associated with the treatment and analysis of psychophysiological measures. In all instances within this program of research, all physiological data was individually visually scanned for the presence of movement artefacts, signal interruption and data loss. In the event of an artefact, it was necessary to match the timestamp available in the numerical printouts with the occurrence of the artefact in the physiological trace; the compromised data corresponding with the timestamp was then removed from analysis. Including the steps taken to analyse and extract the data from the physiological recording software, each physiological measure required between 10 and 20 minutes of treatment per participant. This process was further extended by the rendering time required for frequency analysis of EEG, in which each site required seven minutes of computer processing time to output the data for all frequency bands per participant. This totalled 21 hours of computer processing time for frequency analysis on two sites alone; in this program of research, this procedure was carried out on four high-end PCs to reduce this time cost. Finally, all data required conversion to a readable format and layout for analysis within SPSS; as each measure represented hundreds of cells of data across three conditions, this process also represented a notable temporal cost. The estimated total time invested in non-
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statistical analysis, treatment and set-up of all physiological measures in the current program of research was approximately 300 hours. The breakdown of this is available in Table 13.
Table 13. Overview of temporal costs for psychophysiological measures
EDA ECG EMG EMG EEG EEG Total OO CS AF4 O2
AcqKnowledge 5 mins 10 mins 5 mins 5 mins 10 mins 10 mins ‒ Analysis
AcqKnowledge ‒ ‒ ‒ ‒ 7 mins 7 mins ‒
Render Time
Kubios ‒ 5 mins ‒ ‒ ‒ ‒ ‒
Analysis
Artefact 10 mins 5 mins 15 mins 15 mins 20 mins 20 mins ‒ Checking
Analysis Set- 5 mins ‒ 5 mins 5 mins 15 mins 15 mins ‒ up
Total per n 20 mins 20 mins 25 mins 25 mins 52 mins 52 mins 194 mins
Sample Total 30 hrs 30 hrs 37.5 hrs 37.5 hrs 78 hrs 78 hrs 291 hrs
In the interest of minimising time cost for future research in both academic and commercial contexts, some measures were found to be less time-consuming in set-up and analysis than others.
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Furthermore, resources are available—although they were not employed in this research program due to financial or methodological concerns—that also reduce time costs.
In terms of epoch, HR and HF analysis, EDA and ECG proved the most time-efficient measures; in terms of EDA and ECG, movement artefacts are more instantly recognisable than artefacts that may occur in EMG or EEG data, in which some artefacts may be obfuscated by natural oscillations of the trace (EEG) or emoting (EMG). ECG movement artefact analysis is further aided by the use of Kubios software, which features a rigorous automatic artefact correction algorithm and respiration frequency analysis that ensures the HF component remains within the
HF band limits (Tarvainen et al., 2013)—thus limiting the need for manual removal of minor artefacts.
While disposable electrodes were not used for EMG and ECG analysis within this research program, the use of disposable electrodes would minimise the time costs associated with electrode maintenance and preparation. Pre-filled disposable electrodes would also minimise the risk of air bubbles in freshly applied electrode gel compromising the physiological data.
Finally, both software and outsourcing is available for detecting movement artefacts.
While these approaches were not employed in this research program due to financial limitations, automatic movement artefact detection software or outsourcing to laboratories that offer this service would also minimise the time cost of psychophysiological data in contexts where it is accessible.
6.4.2 Viable Psychophysiological Approach
This program of research investigated the utility of multiple psychophysiological measures in the assessment of the player experience. In an effort to address the primary research question and research aim, each measure will be critically assessed in terms of interpretability,
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temporal costs, effect size, and accessibility within the context of their use in this research. It is intended that this assessment will prove helpful in informing experimental design for researchers and developers considering a psychophysiological approach in future player experience evaluations. For an overview of this discussion, please refer to Table 14.
Table 14. Overview of utility of psychophysiological measurements
Interpretability Time Cost (per n)* Effect Size(s)
2 EDA Relatively 25 mins ηp = .081 interpretable; some findings contradict existing research
2 ECG Complex, but multiple 25 mins ηp = .071 - .102 paths for analysis allow for greater detail
2 EMG Relatively 25 - 30 mins ηp = .279 interpretable; could be aided by phasic analysis
2 EEG Complex; in this 60 mins ηp = .137 - .389 research, some intuitive and some non-intuitive results
* includes set-up, treatment, and analysis
Overall, EDA proved the most efficient measure in terms of interpretability and temporal cost. The EDA measure also featured the lowest rate of data loss within this program of research, with only four of 90 samples deemed unsuitable for analysis; this situates palmar EDA as particularly robust to data contaminants or dislodgement. The relative ease, deployability, and
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interpretability of EDA, as well as its sensitivity to psychological stimuli, is reflected in its widespread and stable rate of employment in psychophysiological and psychological research
(Dawson et al., 2000). This high rate of adoption is echoed in player experience and games research, with the majority of studies reviewed in section 2 utilising EDA. The prevalent employment of EDA in research literature represents a considerable advantage to researchers considering a psychophysiological approach, particularly those unfamiliar with the psychophysiological assessment. However, EDA produced one of the smallest effect sizes within this program of research, possibly indicating limitations in the use of its assessment of small differences between experiences; as such, despite its efficiency and robustness, EDA does not necessarily allow for granular psychophysiological insight in the context of challenge-skill balance manipulation. It should be noted that this conclusion should not be broadly applied to the study of all concepts: as discussed in section 5.6, manipulation of separate phenomena—for example, violence or sound—may yield different results, such as improved granularity.
Similar degrees of efficiency and accessibility are found for ECG (as EDA); however, this is moderated somewhat by application and analysis. ECG is necessarily more invasive than palmar
EDA, requiring application to the torso of the participant—which can in turn amplify potential for data loss in instances of increased adipose tissue, or the risk of dislodgement of electrodes unknown to researcher or participant as a consequence of clothing obscuring the recording sites.
Despite this, ECG featured the second lowest rate of data loss within this program of research. In terms of interpretability, the seemingly contradictory results of both HR and HRV HF Peaks reported in this thesis suggest caution in the intuitive interpretation of results. While not unique to
ECG, core psychophysiological constructs—such as the startle response, anticipatory response, directional fractionation, and habituation—are prone to influencing results, and warrant careful interpretation and analysis of findings. Fortunately, multiple paths for ECG analysis (e.g. HR, R-
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R, and HRV analysis) allow the opportunity for a granular and considered analysis approach. As such, ECG situates itself as one of the more time-effective psychophysiological approaches while simultaneously allowing for nuanced evaluation. Despite this, as with EDA, ECG featured some of the lowest effect sizes reported within this program of research—once again indicating limitations in the detection of small differences between experiences within the context of challenge-skill balance manipulation. ECG may prove most useful for researchers interested in a simultaneously efficient and robust psychophysiological assessment, notwithstanding possible complexities associated with the interpretation of results.
With respect to EMG (taken from the OO site), the strength of the effect size, as well as the interpretability of results, point to its strengths as a granular measure of the player experience in the context of challenge-skill balance manipulation. Although EMG is typically used for phasic analysis (through the study of reactive expressions to specific events), these results also establish the usefulness of EMG in tonic analysis; however, phasic analysis would introduce greater clarity to these results through the investigation of a possible startle response. Despite this, the EMG measurement featured the greatest loss of data within this program of research—which may also account for the absence of results altogether for EMG CS. This loss of data was likely the result of poor electrode contact or electrode dislodgement at some point throughout the experience, and a failure to recognise realtime data loss during experiment runtime. This issue may have been circumvented through the use of more rigid equipment (during data collection it was noted that, during setup, the ADD204 adhesive collars would often peel or crinkle on the surface of the skin during moments of expressivity) and familiarisation with the appearance of data contamination in real-time. Should these issues be resolved, however, EMG still proves costly in terms of temporal investment: in setup, researchers need to be rigorous in ensuring they are accurately locating the precise muscle site for analysis and may need to continually refer to a facial muscle chart
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throughout experimentation to guarantee correct placement. Throughout data collection within this program of research, both EMG sites were more prone than other measures to rates of greater impedance on first-time application; this impedance was often high enough to warrant re- preparation and re-application of the EMG electrodes. Finally, the distinction between movement artefact and facial expressivity proved to be a time-consuming process in data treatment. As such, the use of EMG is not readily recommended for time-poor studies or researchers not previously familiar with psychophysiological assessment.
While results for EEG presented one of the highest effect sizes in the program of research, outstripping all physiological and most subjective measures, the interpretability of results draws into question the suitability of EEG—or perhaps more precisely, the EMOTIV Epoc—for player experience research. Furthermore, while the application of the headset proved one of the least time-consuming aspects of psychophysiological setup, the time cost and hardware resources associated with frequency band analysis and data treatment offset this (particularly as each frequency band for each site represents its own unique physiological measure, requiring individual treatment and preparation). While the results EEG offers are rich and detailed, the associated time costs, complexity of analysis and interpretation, and limitations of the EMOTIV Epoc (see section
5.5.6.4) situate this measure as potentially too restrictive in player experience research or playtesting contexts.
Overall, the findings from the current program of research confirm that psychophysiological assessment allows for objective and interesting insights otherwise unattainable by typical evaluative measures within games research and evaluation (e.g. survey, observation); however, the use of psychophysiology should be carefully considered in the context of study aims and resources available to the research team. As the capability for distinguishing
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small differences between experiences increase, so too does the complexities and resource-cost associated with the psychophysiological measurement.
6.4.3 Sample Sizes
As discussed in section 2.9, prior player experience research employing multiple contemporaneous psychophysiological measures has often featured small sample sizes. This limitation emerged as a consequence of the substantial temporal investment required for the attachment, treatment and evaluation of psychophysiological measures.
The question of sample size was addressed by this research program in two distinct ways: as the conclusions suggest that psychophysiological assessment is limited in its capacity to predict subjective experience in the context of challenge-skill balance manipulation, the evaluation of granular differences may only be reliably achieved by obtaining a much larger sample size than that reported in Study 2. This pushes the bounds of feasibility within current psychophysiological research. However, commercial research has previously investigated the integration of non- invasive physiological measures with video games hardware (such as controllers); this would allow for a passive, remote assessment of psychophysiological response, which may generate the sample size required for the psychophysiological distinction between more granular constructs.
The understanding of psychophysiological assessment as best suited for measuring large differences also provides evidence in favour of sample sizes that do not necessarily exceed what is required for assessing differences. It should be emphasised, however, that the limitations in detecting small differences has only been established within this research in the context of challenge-skill balance manipulation; future research may find greater success in the psychophysiological detection of granular experiences.
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6.4.4 Data Quality Checks
The notable loss of data for both EMG OO and EMG CS establishes a clear need for adapting psychophysiological methodologies to include consistent psychophysiological analysis and quality checks throughout data collection. Furthermore, where possible, the visual familiarisation among researchers in the player experience space with a compromised or unclear physiological signal would also aid in the early detection of unusable data. It is thus proposed that physiological data should be treated and analysed every five n (dependent on intended sample size); extra care is recommended for the collection of facial EMG data.
6.4.5 Automation and Reduction of Participant‒Researcher Interaction
One of the key strengths of this research program lay in automating the experimental process and reducing participant‒researcher interaction, which has been revealed as problematic in prior psychophysiological player experience research (Mandryk et al., 2006b). The Sequencer software notably reduced the risk of human error in the experimental process, ensuring that the appropriate condition and questionnaire order was maintained throughout and preserving consistent runtime of all baseline and play conditions. Furthermore, the Sequencer software allowed the researcher to remain physically removed from participants, with all interaction limited to short instructions throughout (e.g., ‘Please press continue.’). This approach notably minimised the potential for human interaction to influence physiological response, and is strongly recommended for ongoing psychophysiological research in the player experience space. Although not feasible for this research program, live recording of the participant’s screen would allow for the researcher to be removed altogether to a separate room once data collection had commenced.
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6.4.6 Familiarisation with Psychophysiological Principles
Player experience research should remain aware of fundamental psychophysiological concepts in the evaluation of physiological data. Concepts such as habituation, startle response, stimulus-response specificity, directional fractionation, one-to-many and many-to-one domain relationships, and the myth of the unidimensional continuum of arousal should moderate the analysis and interpretation of psychophysiological response. As discussed in 2.9.6, studies in the player experience research space are sometimes limited in their consideration of these concepts as potentially influential on, or responsible for, the presented results; therefore, it is advisable to moderate conclusions with the general principles of psychophysiological interpretation.
6.4.7 Summary
Psychophysiological assessment allows for a quantitative, objective and insightful evaluation of the player experience (Mandryk et al., 2006b; Bernhaupt et al., 2008). Using psychophysiological measures may allow for experiences not detected through subjective measures to be identified. In this research program, increased HR in the Boredom condition suggests that the experience—despite largely being reported as sub-optimal, or boring, by players—may also be influenced by anticipatory response. Additionally, psychophysiological analysis also allows for an additional dimension in the interpretation of subjective results—while players generally reported the Overload condition as sub-optimal in terms of interest/enjoyment, presence, competence, autonomy and flow, increased EDA and decreased HF peaks in this condition position it as a high-arousal experience. The use of psychophysiological measures in conjunction with subjective measures allows for a more nuanced understanding of the player experience.
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The tonic interpretation of psychophysiological response should be mediated by the consideration of stimulus-response specificity, habituation and domain relationships—for example, the many-to-one relationship. While employing subjective measures may help to clarify these experiences, care should be taken in interpreting physiological response as indicating certain psychological states. This is further highlighted by the limitations in assessing predictive relationships between psychophysiological response and specific self-reported components of the player experience.
Overall, psychophysiological assessment allows for a greater understanding of the player experience than that obtained through subjective evaluation alone. However, as the direct psychological stimulus for physiological response is rarely conclusive, care should be taken in tonic psychophysiological evaluation—n approach employing both psychophysiological and self- report measures allows for a clearer and more comprehensive interpretation of the player experience.
6.5 LIMITATIONS AND FUTURE RESEARCH
This research has provided insight into the psychophysiological response to optimal and sub-optimal play experiences, psychophysiological methodologies within the player experience evaluation space and the applicability of psychophysiological measures in evaluating the player experience within academic and commercial contexts. However, as revealed by analysis and discussion, there are methodological limitations that leave some components unexplored.
As the Overload condition was designed to overwhelm, frustrate or evoke anxiety in participants, a semi-counterbalanced design was introduced to mitigate the potential for a long- lasting negative mood induction inadvertently influencing the remaining conditions. Within the
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semi-counterbalanced design, the Overload condition was placed to always occur last in condition order; both the Boredom and Balance condition were fully counterbalanced. Consequently, potential exists for order effect to influence the player experience data collected from the Overload condition. One potential impact of the semi-counterbalanced approach may be the possibility for physiological drift to influence EDA results. As EDA presents an additive signal, it is possible for the signal to continually increase overtime over the course of the experiment; as such, this could potentially influence the increased EDA signals found in the Overload condition. However,
Braithwaite et al. (2015) note that drift is primarily only an issue in long-term experiments, and especially long-term ambulatory experiments; furthermore, the impact of potential drift has been somewhat mitigated by the inclusion of baselines between each play session. Despite this, future research may entail further data collection within a fully counter-balanced version of the current study methodology, as well as the use of the baseline data as a calibration point for EDA analysis and investigation of physiological drift.
Additionally, the Overload condition was the only condition in which player death was unavoidable. As each player death represented up to 15 seconds of removal from play (in which the screen would fade, and reset the player to the beginning area), disengagement from the play experience during this period may have occurred. While player deaths were also possible in the
Balance condition, they were not ensured by the paradigms of the condition design (and were impossible in the Boredom condition). The increased death rate in the Overload condition may thus have similarly increased the risk of disengagement.
Due to limitations in the time available for analysis within the candidature and consideration for applicability to player experience evaluation contexts, physiological analysis was restricted to tonic evaluation. Mandryk et al. (2006b) identify the prohibitive temporal costs
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associated with phasic analysis and associated video coding in their own studies of eight and 10 participants; as this study featured an n of 90, tonic analysis was chosen as the most suitable method for physiological evaluation. However, as discussed in section 5.5.4, this does have implications for the assessment of EMG—a measure that is typically evaluated phasically, and does not consistently allow for post-hoc distinction between facial movements. Phasic analysis of the available data, particularly EMG, will be considered in further exploration of the program’s results moving forward. Future research may also benefit from an aggregate approach, as used by
Hazlett (2006), in which Hazlett analysed the physiological response to video game events that were previously identified by expert participants as either ‘negative’ or ‘positive’
An additional analysis approach that may be insightful in terms of explaining results is a comparison of physiological timebins. This is especially applicable to physiological analysis of the Overload condition, in which habituation or physiological detachment were identified as potentially influential on HR data. In particular, HR analysis and comparison of the first and last five minutes of the Overload would allow for a more complete understanding of the psychophysiological response to the condition.
The analysis of the EEG sites was also limited as a result of research scope considerations.
While the EMOTIV Epoc EEG headset provides 14 channels (each recording from a specific site), only two channels—or two sites—were selected for frequency analysis. Furthermore, analysis was restricted to the alpha, beta and theta frequency bands. Complete analysis of all sites and frequency bands (alpha, beta, gamma, theta and delta) would have resulted in the generation of 70 variables for inclusion in the analyses described in section 5.1.8. Due to limitations in laboratory equipment, each site and frequency band required manual scanning to detect and remove movement artefacts.
Analysis was thus restricted to two sites located on the right hemisphere, and three frequency
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bands (or six variables), chosen for reasons described in section 3.3.3.1. Depending on the determination of the EMOTIV Epoc EEG headset’s usefulness in this research program, the analysis of these additional sites and frequency bands presents an opportunity for a more granular understanding of the electroencephalographic response to player experience.
Unfortunately, a considerable amount of data was lost from both the OO and CS EMG sites. This reduced sample size may have been responsible for the absence of results discovered for the CS site in particular. The loss of data was likely a result of poor electrode contact, electrodes shifting throughout the experiment or a fault in one of the electrodes in circulation; an alternative, although less likely, explanation is signal interference at the laboratory site. Regrettably, the poor data quality was unrealised during collection due to lack of visual familiarity with compromised
EMG signals; this issue was thus not discovered during occasional exploratory analysis throughout data collection. Thorough analysis and checking of physiological data, as well as familiarisation with compromised signals, is recommended throughout data collection in future research. As
EMG proved especially sensitive to noise and data loss within this research program, special care is advised for employment of this measure.
Additional physiological measures not employed in this research program may also help to shed light on the current understanding of psychophysiological response to player experience.
Omitted from Study 2’s methodology for temporal, financial or practical reasons detailed in section 3.3.3 are measures that include BP, respiration, salivary cortisol, eye-tracking, body temperature analysis and the ZM site for EMG. Including these measures in future research would enable a more granular understanding of the psychophysiological player experience.
In terms of participant demographics, female participants were notably underrepresented, with 22.5% female participation in Study 2; this contrasts with data available on video game player
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demographics, indicating that between 44% (Entertainment Software Association, 2015) and 47%
(Brand & Todhunter, 2015) of video game players are female. It is unknown whether the gender ratio for Study 2 affected the generalisability of results. This gender imbalance points to limitations in sampling from undergraduate STEM cohorts, in which women are disproportionately underrepresented (Hill, 2010). As under-employment of female participants has been identified as a consistent limitation in player experience research (Järvelä et al., 2015), there is a recognisable need to address gender representation in future research in this space.
Gender likewise represents a potential limitation in the current interpretation of results.
Some research suggests that gender does play a role in, and may influence, psychophysiological results; consequently, many psychophysiological studies are performed using a male sample only
(Stern, 2001). Bianchin & Angrilli (2012) suggest biological grounding for greater sensitivity and vulnerability to adverse or stressful events amongst women, manifesting in increased startle reflex amplitude and modulation in the amygdala and orbitofrontal cortex. In their investigation of children’s psychophysiological response to violent video games, Gentile et al. (2017) found increased cardiovascular activity in response to the violent condition amongst boys only. Despite this, Gentile et al. note that similar studies have not returned this gender disparity in results, and so suggests further investigation. Future analysis of the results reported within this thesis would benefit from separate treatment of data by gender, for the dual benefits of investigating a potential gender influence on results and contributing to current understanding of the role of gender in psychophysiological measurement and analysis.
As for the generalisability of results, the video game conditions used for this research program were restricted to a single-player first-person shooter PC game. Future studies in this space may benefit from expanding this research to include additional genres, environments and
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platforms in the interest of results’ generalisability. Furthermore, as human interaction has been found to have a profound effect on physiological response (Mandryk et al., 2006b), future research may benefit from a large-scale psychophysiological evaluation of a social play experience.
The difficulties of assessing flow in an easy or unchallenging play experience point to a clear need for continued assessment of the construct, as well as the S FSS and FSS-2 flow scales, in terms of applicability to player experience evaluation. As the S FSS and FSS-2 are closely matched to Csikszentmihalyi’s (1990) criteria for the experience of flow, there may be merit in considering whether these criteria can be directly applied to evaluating the video game player experience. This is particularly pertinent due to the prominence of the flow construct in player experience literature (Cowley et al., 2008). Furthermore, continued exploration of the role of challenge‒skill balance in evoking flow is recommended; the results of this research potentially further support recent research disputing the concept of challenge‒skill balance as an antecedent to flow (Fong et al., 2015; Rheinberg et al., 2003). Similarly, the assessment of competence within the PENS scale yielded comparable difficulties in distinguishing between boring and optimal play experiences. This suggests caution in interpreting high competence as reported by the PENS scale as indicating optimal play experience. This route of investigation should also be applied more generally to the assessment of unchallenging games or optimal play experiences, with some conclusions drawn from this research suggesting the possibility for players to invent their own
‘fun’ or challenge within inherently un-fun play experiences.
The potential for player-created challenge may be amplified within experimental settings, as the option to simply quit playing as a result of boredom is not always available. Mandryk et al.
(2006b) note a similar phenomenon in the easy condition generated for their study on psychophysiological techniques; the authors recount players creating their own challenge within
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NHL 2003 by introducing personal rules such as scoring a goal with only defensemen. This is also true for overly-challenging experiences: in an experimental setting, the player is obligated to continue playing or may feel under social pressure to self-moderate negative response to difficult play experiences. As such, the Overload and Boredom conditions may not be completely generalisable to a typical player experience due to their experimental context. Consequently, the artificiality of the laboratory environment is another limitation of this program of research (as well as most extant player experience literature). Future psychophysiological player experience research may benefit from the employment of naturalised experimental settings that encourage autonomous interactions with play conditions. Another option may be the remote collection of physiological data via wireless worn devices, so that participants may play the video game conditions at home.
Finally, the data obtained during this research program did not allow for the psychophysiological identification of small changes in specific components of the player experience. A combination of data loss from some psychophysiological measures, as well as the low variances for HF, EMG OO and EEG, resulted in a dearth of results for both correlations and regressions analysis. This may suggest that, in terms of predictive analysis, psychophysiological measures may be limited in their detection of small differences in play experiences in the context of challenge-skill balance manipulation. It should be noted that this is not currently generalisable to the study of other player experience phenomena; future research of other video game concepts and traits, such as the manipulation of sound or graphical violence, should incorporate predictive analysis to determine whether this result is also true within the context of those specific manipulations. However, within the context of challenge-skill balance manipulation, the limitation in the detection of small differences may be ameliorated through the collection of a larger sample size. While the collection of a notably larger sample size than that featured in this
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research is not feasible within most academic and commercial contexts, including non-invasive sensors for collecting physiological data in games hardware (e.g., controllers) would allow for the passive collection of psychophysiological data from a far larger sample pool. Research in this space has already been attempted by video game development companies Nintendo, Sony and
Valve (Patel, 2009; Findlater, 2013; Ambinder, 2011). The passive obtainment of a substantial sample size would generate a more refined understanding of the psychophysiological responses to play.
6.5.1 Future Approaches to Analysis
A tonic approach was selected for this program of research in alignment with extant player experience literature, and out of consideration of time constraints; however, future interpretation of the data presented within this thesis may also benefit from more granular or phasic analysis.
This section will detail several approaches that could be undertaken that will allow for further exploration of the data collected within this program of research, and that may grant further clarity to the current interpretation of the results.
Further investigation of the unexpected EEG O2 Alpha and HR results may benefit from a more phasic analysis in the interpretation of initial and concluding timebins. In particular, a frequency analysis of a select period (e.g. 3—5 minutes) from near the start of each condition and from the end of each condition may reveal a pattern of results indicating habituation, or anticipatory response, as a possible explanation for the results. For example, significantly increased HR in the Boredom condition may only emerge as a result for the initial timebin—which would further support the conclusion of the role of anticipatory response in the interpretation of this data. This analysis approach could also contribute to an understanding of the player experience as it changes over time. Participant recollection of experiences is often influenced by a peak-end
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effect, in which the ‘peak’ (or climax) and the conclusion of the experience colours the subjective response to the experience (Gutwin, Rooke, Cockburn, Mandryk, & Lafreniere, 2016); as such, real-time psychophysiological exploration of the experience as it differs over time may present novel findings that improve current understanding of player experiences.
A phasic analysis of events may also present the opportunity for clarification of the findings drawn from the tonic analysis; however, it is important to note that neither study within this program of research was designed for phasic analysis. As such, all play conditions lack the discrete events that typically occur in video game artefacts intended for phasic analysis—such as picking up tokens, or falling off the map, in the phasic psychophysiological research undertaken by Ravaja et al. (2006). Furthermore, as challenge and challenge-skill balance were a primary focus of this research, the introduction of DDA to the Balance condition resulted in differing combat experiences per participant in the interest of maintaining consistent challenge (e.g. increased or fewer special enemies, increased or decreased combat duration). Despite this, one potential path for phasic analysis is that of the deaths that occur in the Overload condition: analysis of physiological response to repeated player deaths may help to explore conclusions offered by
Ravaja et al. in earlier research (2008).
Normalisation of data represents an additional route for further analysis of the data collected within this program of research. Mandryk (2008) recommends the normalisation of physiological data – or representation of physiological data as a percentage of the timespan – for improved contextualisation and interpretation, and reduction of the impact of individual physiological differences (Mandryk, 2008). It is possible that normalising the collected data may allow for some patterns to emerge when investigating individual correlations that were not otherwise accessible
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without applying normalisation techniques. As such, normalisation represents a natural future step in analysis.
Further research could include more detailed analysis through the interpretation of clusters of data. One route for exploring the data obtained during this research program is to consider possible differences between the psychophysiological responses of novice and expert players, which was not assessed here. Research in the psychophysiological space has identified expertise as having influence on the physiological response (Fairclough, Venables, & Tattersall, 2004;
Cooke et al., 2014), with evaluation of the player experience having more specifically identified associations between expertise in video game play and decreased emotional expressivity as recorded by EMG (Weinreich, Strobach, & Schubert, 2015). A large-scale psychophysiological comparison of novice and expert groups within video game play would broaden current understanding of the psychophysiological response to player experience. Other options for exploration may include age, participant familiarity with the chosen game artefact and participant familiarity with genre.
6.6 CONTRIBUTIONS TO KNOWLEDGE
This program of research has explored the utility of the psychophysiological assessment of the player experience and has contributed recommendations for ongoing psychophysiological approaches in terms of the selection of physiological measures, experimental design, analysis, and data collection. The findings driven by both the subjective and psychophysiological data have further contributed to extant psychophysiological player experience literature in the exploration of relationships between challenge and physiological response, finding generally increased physiological activation in instances of high challenge (with some exceptions). These findings have both supported and contrasted with existing research, and consideration—based on core
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psychophysiological concepts—has been given to why this might be. This has notably fulfilled a component of the overarching aim for this program of research, which sought to ‘… further clarify existing contributions to literature’.
The finding of generally increased physiological activation in instances of high challenge presents several implications for both industry and research. In the development of biofeedback and physiologically-informed DDA, researchers and developers will find greater assurance in the assumption that increased challenge will result in increased arousal; as such, there is improved credence to the concept of the dynamic adjustment of difficulty in response to decreases and increases of arousal. In terms of player experience assessment, researchers and developers may benefit from this knowledge when assessing the difficulty of their game. Despite this, as suggested by some results in this program of research, it is pertinent that researchers and developers remain aware of the potential for habituation – and that a sudden decrease in arousal may not necessarily indicate a decrease in challenge.
Relatedly, this thesis contributes several methodological recommendations for psychophysiological analysis within the field of player experience evaluation. Chief among these is the careful consideration of core psychophysiological principles, such as habituation, stimulus- response specificity, and directional fractionation, which have often been neglected in results interpretation in previous player experience literature. It is hoped that the discussion of these concepts, and how they may influence results, will allow for improvements in the careful consideration of psychophysiological results in future player experience research.
Furthermore, the identification of the benefits and drawbacks of the psychophysiological measures used within this study may help in the development of future player experience methodologies. In particular, consideration of time costs—in terms of both deployment and
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analysis—may be of notable importance to researchers, not least because of the unique requirement in player experience research to limit the potential for participant disengagement or boredom. Additionally, while the results of this research aren’t necessarily generalisable, the performance of the psychophysiological measurements reported within may also be helpful to future researchers developing an experiment methodology: for example, EDA was found to be simultaneously the most time-efficient measure while also having the lowest effect size; in contrast, EEG was the most time-consuming measure while also featuring the greatest effect size
(amongst some frequency bands). While this can vary by equipment, expertise, analysis approach, and subject matter, and is therefore not universal, these findings present a helpful starting point for methodology development and consideration.
As discussed in section 6.4.3, this research also highlights the unique benefits that industry and industrial research possess: the opportunity for the remote, naturalised, and widespread collection of data, allowing for the large sample size required for the psychophysiological distinction between granular constructs. The analysis of such data would notably improve understanding of player bases, allow unique insights for developers in terms of creating optimal play experience, and represent a substantial contribution to player experience literature. The potential contributions of this work extend beyond player experience, and carry implications for broader HCI and psychology research. As growing literature employs games as a stimulus for studying human behaviour (Järvelä et al., 2015), there is greater emphasis on understanding the fundamental artificiality of the laboratory and movement towards richer stimulation paradigms that are closer to real world experiences. As such, the contained consideration and review of a psychophysiological method employing a video game as the core stimulus may prove beneficial for research for which video game design and player experience analysis is not the research objective. Similarly, challenge is not a construct unique to player experiences—a natural analogue
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is challenge that occurs in learning or vocational environments. As such, contributions to the psychophysiological understanding of challenge may also be useful to research investigating the effects of differing challenge levels, or over- and underwhelming challenge, on students and workers. Finally, the consideration of the utility of psychophysiological assessment—and recommendations for best practice—is useful to any researcher who may be considering a psychophysiological approach in their own work, regardless of academic or commercial contexts.
6.7 CONCLUSION
This research expanded upon the understanding of psychophysiological experience of play, as well as the utility of physiological response as a means of assessing the player experience.
Stage 1 (Chapter 3) laid the foundations of the research program by exploring a viable psychophysiological and psychological approach for the analysis of player experience. Review of player experience and psychophysiological literature identified EMG, ECG, EDA and EEG, as well as the subjective experience of flow, as the initial measures that would allow for a robust exploration of the psychophysiological response to player experiences. An iterative design stage resulted in the creation of three video game conditions—Boredom, Balance and Overload—that differed in terms of challenge‒skill balance, providing a useful tool to compare psychological and psychophysiological responses.
The experimental study conducted in Stage 2 (Chapter 4) investigated the three video game conditions’ ability to invoke or inhibit flow, and found that results did not conform to expectations of condition performance. Namely, flow was not found to differ significantly between the Boredom and Balance conditions. A robust investigation of these results presented several possible explanations, as discussed in Chapter 4.
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In response to these findings, several changes were made to the study methodology and condition design. The psychometric approach was adapted to include additional measures
(interest/enjoyment, competence, autonomy and presence), with the assessment of flow restricted to a shorter scale to minimise experiment runtime. The challenge‒skill condition manipulation was maintained, as challenge remained a crucial element in ensuring optimal play experiences.
The Boredom condition was further modified to ensure the absence of game challenge, with all semblance of combat—in the form of enemy combatants—removed from the game. Furthermore,
Stage 2 oversaw the development of the Sequencer and baseline experimental software to improve the rigour of physiological analysis. Both programs were designed to minimise participant‒ researcher interaction, ensure consistent experiment, baseline and condition runtimes, and guarantee appropriate condition and survey order.
Stage 3 (Chapter 5) found that the condition design was generally successful in creating optimal and sub-optimal conditions, with the Balance condition emerging as the intended optimal player experience. In all self-reported measures, Balance received the highest of equal highest results. For the flow and competence measures, Balance was matched by Boredom in this achievement, but was at no point outstripped by Overload. These results support and expand upon the crucial role of challenge and challenge‒skill balance in creating optimal player experiences.
However, interesting implications for assessing sub-optimal conditions—especially conditions in which the skill of the player radically outstrips the demands of the game—also arise. These findings suggest the potential for player-generated challenge fun in boring or underwhelming conditions. Not exclusive from this conclusion is also possible limitations of the PENS competence and the Jackson FSS-2 and S FSS-2 in assessing boring or easy video game artefacts, as the items often do not allow for a clear distinction between challenge‒-skill balance and skill > challenge imbalance experiences.
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Analysis of physiological response expands on the current understanding of the psychophysiological response to player experiences, and reveals results both congruent and incongruent with current player experience research. Results for the Overload condition reveal significantly increased EDA and decreased HRV HF peaks than in the Boredom or Balance conditions; furthermore, they featured significantly increased HR than the Overload condition. As previous psychophysiological research in the player experience space found no main effect of difficulty on EDA, HR or HRV, these results represent a novel contribution to the field. EMG OO activity was found to be significantly greater in the Overload condition than either the Boredom or Balance conditions, tentatively supporting previous player experience research that found decreases in OO activity associated with boring player experiences. This supports EMG OO as a potentially reliable measure of experienced difficulty. Finally, the findings for the EEG frequency bands may suggest states of greater mental and emotional processing, attentional focus, creativity and potentially negative affect in the Balance and Overload conditions than in the Boredom condition, linking these cognitive states with increased challenge (or suggesting the inhibition of such states in boring or unchallenging play experiences).
The analysis and discussion of the data gathered during Stage 3 also allowed for the evaluation of the successes and limitations of the methodology employed for this research program.
Furthermore, applying general psychophysiological concepts—such as stimulus-response specificity, directional fractionation or the consideration of one-to-many domain relationships— facilitated analysis informed by psychophysiological practice and literature. These findings enabled the generation of a series of recommendations, cautions and opportunities for future psychophysiological research in the player experience space (discussed in sections 6.4 and 6.5), particularly regarding practical concerns (such as time costs) and recommended caution in interpretation. Generally, the analysis of psychophysiological response was found to be useful for
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generating insightful, objective conclusions regarding the player experience; however, these results are tempered by the limitations of the inconclusive nature of physiological response, and recommendations are made for methodological approaches featuring both psychophysiological and subjective measures.
6.8 FINAL COMMENTS
As video games continue to grow as a leading form of entertainment, so too does the value of understanding the player experience. The use of psychophysiological assessment in a context typically evaluated by subjective and self-report measures allows for a broader understanding of the player experience, and enables exclusive objective insights that complement subjective evaluation. This program of research represented an effort to further expand psychophysiological understanding of the player experience through a large-scale approach, featuring a larger sample size and greater breadth of physiological measures than was currently available in player experience literature. This thesis presents novel contributions in the form of psychophysiological relationships with challenge and challenge‒skill balance, an important element of optimal play experiences. Furthermore, this thesis highlights the challenges and benefits of psychophysiological analysis, and contributes methodological and interpretative recommendations for future psychophysiological evaluation in player experience research.
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8 APPENDICES
8.1 APPENDIX A—FSS SAMPLE QUESTIONS
As the FSS and S FSS-2 are commercial scales, they cannot be published. Five sample questions from the FSS are thus provided below. Items are scored using a 5-point Likert scale from ‘strongly disagree’ to ‘strongly agree’.
I felt I was competent enough to meet the demands of the situation.
I did things spontaneously and automatically without having to think.
I had a strong sense of what I wanted to do.
I had a good idea about how well I was doing while I was involved in the task/activity.
I was completely focused on the task at hand.
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8.2 APPENDIX B—EXAMPLE PENS ITEMS
As the PENS is a commercial scale, it cannot be published. Three sample items from the PENS are thus provided below. Items are scored using a 7-point Likert scale from ‘do not agree’ to ‘strongly agree’. Thinking about playing, reflect on your play experiences and rate your agreement with the following statements:
I experienced a lot of freedom in the game (autonomy).
I feel very capable and effective when playing (competence).
When moving through the game world I feel as if I am actually there (presence).
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8.3 APPENDIX C—IMI INTEREST/ENJOYMENT SUBSCALE ITEMS
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8.4 APPENDIX D—DEMOGRAPHICS QUESTIONNAIRE
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8.5 APPENDIX E—STUDY 2 SCRIPT
EXPERIMENT OVERVIEW
00.00 – 05:00: Introduction; brief; receive signed and dated consent forms 05.00 ‒ 10:00: Demographics questionnaire 10.00 – 12.00: Participant hands wash 12.00 – 50.00: Biometrics set-up (refer to set-up guide, below) 50.00 – 52.00: Baseline (FLAG: F1 START, F2 END) 52.00 – 57.00: Tutorial (FLAG: F7 START, F8 END) 57.00 – 59.00: Baseline (FLAG: F1 START, F2 END) 59.00 – 70.00: First play session (FLAG: F5 START, F6 END) 70.00 – 77.00: First surveys 77.00 – 79.00: Baseline (FLAG: F1 START, F2 END) 79.00 – 90.00: Second play session (FLAG: F5 START, F6 END) 90.00 – 97.00: Second surveys 97.00 – 99.00: Baseline (FLAG: F1 START, F2 END) 99.00 – 110.00: Third play session (FLAG: F5 START, F6 END) 110.00 – 117.00: Third surveys 117.00 – 119.00: Baseline (FLAG: F1 START, F2 END) 119.00 – 124.00: Remove electrodes, etc. 124.00 – 126.00: Debrief PRE-EXPERIMENT
- Set up nurse’s trolley: o EMG/ECG electrodes o nuprep o tape o conductive gel o scissors o alcohol wipes o cotton buds o gauze pads. - Soak all EEG electrode pads. - Ensure sink is filled and running. - Make sure Left 4 Dead 2 doesn’t need to patch (patch if required). - Initiate Sequencer and pre-fill in participant ID. - Write participant number on post-it note; attach to participant desk. - Have consent form + pen ready. - Launch AcqKnowledge
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o Recent > AllChannels.gtl.
PARTICIPANT ARRIVAL:
- Welcome them and read intro script. - Receive signed and dated consent forms. - Get them to answer demographics questionnaire. - Follow set-up below. Tell the participant what you are doing at all times (e.g., which area you are abrading next). Keep them engaged and comfortable.
Biometric Set-up Guide
EDA:
1. Get participants to wash hands; ensure they are dried thoroughly. 2. Attach disposable EL507 electrodes to the palm (green collared electrode on the hypothenar—or pinkie side of the palm). 3. Secure electrodes with tape.
EMG1 (CS):
1. Locate the corrugator supercilii (refer to figure). You may need to touch the participant’s brow area and get them to emote (frown, raise eyebrows) to find it. 2. Abrade area (nuprep, cotton bud). 3. Wipe area clean (alcohol swipe, clean gauze pad). Note: ask participant to close eyes for alcohol swipe. 4. Take an ADD204 collar and peel the bottom waxed paper strip and apply carefully to an EL254S Ag-AgCl electrode. 5. Fill the cavity with electrode gel (GEL100), avoiding air bubbles. 6. Level off excess electrode gel (not with fingers). 7. Repeat for a second EL254S. 8. Repeat for a ground (black lead) EL254. 9. Attach ground electrode to centre of forehead. 10. Attach the pair of EL254S to the CS location. 11. Lead electrode wires behind participant ear to prevent vision obfuscation. 12. Secure with tape. 13. Clip on EMG1 extender to the back of participant’s collar. 14. Plug ground electrode into GND. 15. For electrode closest to nose, plug white lead into VIN+, black lead into corresponding shield port. 16. Repeat for second EL254S electrode, with VINÈ. 17. Check AcqKnowledge for signal.
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18. Check Checktrode (> 10 kiloohms)
EMG2 (OO):
1. Locate the orbicularis oculi (refer to figure). You may need to touch the participant’s eye area and get them to emote (smile, laugh) to find it. 2. Steps 2‒7 in EMG CS. 3. Attach the pair of EL254S to the OO location. 4. Lead electrode wires behind participant ear to prevent vision obfuscation. 5. Secure with tape. 6. Clip on EMG2 extender to the back of participant’s collar. 7. Steps 15–18 in EMG CS.
ECG:
1. Abrade skin approximately 3–5 cm below right collarbone (refer to figure). 2. Abrade skin approximately 3 cm above participant’s lowest left-side rib (aligning with their elbow—refer to figure). 3. Wipe both areas clean with alcohol wipe and clean gauze pad. Note: ask participant to close eyes for alcohol swipe. 4. Steps 4–7 of EMG CS. 5. Attach one EL254S electrode each to prepared sites. 6. Secure with tape. 7. Clip the ECG extender onto the back of the participant’s collar. 8. For collarbone electrode, white lead into VIN+ and black lead into SHIELD. 9. For ribcage electrode, white lead into VIN‒ and black lead into shield. 10. Check AcqKnowledge for signal. 11. Check Checktrode (> 10 kiloohms).
EEG:
1. Launch TestBench software. 2. Place EEG on head; ensure electrodes are in correct position (refer to figure). 3. Adjust headset until all electrodes make contact (green signal on TestBench). 4. If required, re-soak electrode pads; may need to soak participant hair (note: ask permission first, ask them to shut their eyes). 5. Repeat until all green signals acquired.
COMFORT CHECK: Once all instruments are set up, ensure they are comfortable (no pressure, poking or sensation of electrode detaching). DATA COLLECTION + SCRIPTS
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- Start screen recording. - Initiate Sequencer (press ‘Next’).
DURING BASELINES:
First time: ‘The screen is about to go blank for a short period of time. When it does, please look at the screen and try to relax. While doing so, try to remain relatively still and not tap your fingers, feet and so on.’
All others: ‘The screen is about to go blank again. Again, I ask that you try to relax and not tap fingers, feet and so on.’
DURING TUTORIAL:
‘The objective of the game is to fill a generator with fuel canisters. Enough fuel will lower a bridge and grant you access to a car, completing the mission. The fuel canisters are scattered throughout the map. While exploring, you will likely encounter enemy zombies that you may need to shoot.
I ask that you please do not change any settings such as key bindings.’
BEFORE FIRST PLAY SESSION:
‘I ask that you try and complete the mission to the best of your ability. If you die at any point during any of the play sessions, just keep playing—the level will restart.
Above all else, your main goal here is to try to have fun.’ POST-EXPERIMENT
- Dispose of all rubbish. - Clean EMG/ECG electrodes with toothbrush + tepid water. - Disinfect EMG/ECG electrodes for 5 minutes in disinfectant. - Rinse EMG/ECG electrodes and hang to dry. - Move data onto thumb drive. - Put EEG on charge.
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8.6 APPENDIX F—ELECTROENCEPHALOGRAPHY EEG OUTLIERS
All outliers for untransformed EEG data. In order: AF4 Alpha, Beta, Theta; O2 Alpha,
Beta, Theta.
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8.7 APPENDIX G—BOREDOM, BALANCE, AND OVERLOAD PLAY CONDITION VIDEOS Links to video footage demonstrating play from the Boredom, Balance, and Overload play conditions are contained below.
Boredom: https://www.youtube.com/watch?v=Rr1vr7Drguk Balance: https://www.youtube.com/watch?v=s6dwu0zs8No Overload: https://www.youtube.com/watch?v=rYqRh4zXxwc
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