Videogame Correlates of Real Life Traits and Characteristics
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Videogame Correlates of Real Life Traits and Characteristics. Athanasios Vasileios Kokkinakis PhD University of York Computer Science December 2018 Abstract This thesis attempts to link real life cognitive traits and demographics with in-game player created data. The first experiment focuses on the nicknames chosen by players and the information one can extract from them. Players with nicknames with negative valence (highly racist or vulgar) tend to report others more and receive more reports from other players themselves when compared to their peers. Additionally, many individuals tend to have their real birthdate appended to their nickname (“Jim1986”). This is the first study that has successfully shown this phenomenon. Additionally, by using the extracted dates I showed that negative interactions tend to diminish as one ages. In my second experiment I linked age with in-game performance, as indicated by rank for the videogame League of Legends (LoL). More specifically, performance in LoL tends to peak in one’s mid to late twenties while performance in first person shooters tends to follow a different pattern with an earlier peak; both seem to have a drop after 28. Moreover, I showed that fluid intelligence, as measured by the Wechsler Abbreviated Scale of Intelligence, as well as rotational working memory, which is an overlapping construct, are positively correlated with in-game rank suggesting they are the driving force between the age-rank findings. Finally, I examined personality through the HEXACO framework. One of the most consistent findings in personality research is the existence of Neuroticism or Emotionality which is why it was my choice of focus. Individuals scoring highly on Emotionality, which is a trait linked to anxiety and sentimentality, tend to underperform in the competitive ladder. This was replicated with two videogames of different genres: Hearthstone and LoL. This thesis suggests that we can successfully extract meaningful information at a mass level through commercial videogames. 2 Contents Abstract 2 Contents 3 List of Tables 9 List of Figures 10 Acknowledgements 12 Declaration 14 1. Literature Review 15 1.1.Introduction 15 1.2.Early Research and Military Applications 16 1.3.Academic and non-military Research 24 1.4. Microworlds and Decision Making 26 1.5. Commercial Videogames: MARBLE MADNESS & Tetris 28 1.6. Animal Studies 30 1.7. Mazes 31 1.8. Psychological Tasks as videogames 35 1.9. Commercial Games as IQ tests 40 1.10. Personality and Commercial videogames 42 1.11. Future Research 46 2. Nicknames in League of Legends 48 2.1.Chapter Introduction 48 3 2.2.Introduction 50 2.2.1. Hypotheses 51 2.3.Methods and Materials 54 2.3.1. Data Sources 54 2.3.2. Interaction Valence 55 2.3.3. Antisocial names 56 2.3.4. Age 58 2.4. Results 61 2.4.1. Antisocial names 61 2.4.2. Age 62 2.5.Discussion 63 2.5.1. Summary 63 2.5.2. Antisocial nicknames 65 2.5.3. Age 67 2.5.4. Conclusions 71 2.6. Limitations and Disadvantages 72 3. Videogame Rank and its relationship to Age and Fluid Intelligence 74 3.1. Chapter Introduction 74 3.2.Introduction 76 3.2.1. Intelligence and Task Selection 77 3.2.2. Videogame Selection 84 3.3.Study 1: Fluid Intelligence and associated measures 90 3.3.1. Methods 90 3.3.1.1.Ethics 90 3.3.1.2.Participants 90 4 3.3.1.3.Rank/ MMR /Rating Extraction and Interpretation 90 3.3.1.4.Instruments 91 3.3.2. Results 93 3.3.3. Study 2: Aging and videogame performance across 4 different videogames 95 3.3.4. Methods 96 3.3.4.1. Ethics 96 3.3.4.2. Data sources 96 3.3.4.2.1. League of Legends 96 3.3.4.2.2. DOTA II 96 3.3.4.2.3. Destiny 97 3.3.4.2.4. Battlefield 3 97 3.3.4.2.5. Fluid Intelligence and Aging 97 3.3.4.2.6. Grouping and Standardisation 97 3.3.5. Results 98 3.3.5.1.Outlier Rejection and Descriptive Statistics 98 3.3.5.2. Homogeneity of variance 99 3.3.5.3. One-way ANOVA – Welch’s F 99 3.3.5.4. Contrast Coefficients 100 3.4. Discussion 102 3.4.1. Study 1 103 3.4.2. Practice as an alternative explanation 107 3.4.3. Study 2 108 3.4.4. Age and Practice as confounding variables 108 3.5. Limitations 109 5 3.6. Implications/Contribution 112 4. Emotionality in Videogames 116 4.1. Chapter Introduction 116 4.2. Introduction 117 4.3. Study 1 119 4.3.1. Methods and Materials 119 4.3.1.1. Ethics 119 4.3.1.2. HEXACO-60 120 4.3.1.3. Gaming Questionnaire 120 4.3.1.4. Participants 120 4.3.2. Results 121 4.3.2.1. Missing Value Analysis 121 4.3.2.2. Descriptive Statistics and Distributions 124 4.3.2.3. Rank and Emotionality Correlations 126 4.4. Study 2 126 4.4.1. Methods and Materials 126 4.4.1.1. Ethics 126 4.4.1.2. Participants 126 4.4.2. Results 126 4.5. Discussion 128 4.5.1. Emotionality, its facets and Rank 128 4.6.Limitations and Criticisms 132 4.7. Implications 134 5. Synthesis 135 5.1. Introduction 135 6 5.2.Conclusion 136 5.3. Limitations and problems of videogame research 137 5.3.1. The Blackbox problem and its reasoning 137 5.3.2. Different Genres of video games may tap different abilities and conclusions may not generalise even in the same game across patches and servers. 138 5.3.3. The depreciation of videogame data 140 5.4. Sampling 141 5.4.1. Survivorship Bias and Special Populations 141 5.4.2. Range Restriction 142 5.4.3. Cross-Sectional Vs Longitudinal 144 5.5. Establishing Rank Correspondence with Standardised IQ ranges based on Age 149 5.6. Future Application Areas 149 5.6.1. Theoretical Framework for Education 150 5.6.2. Theoretical Framework for Health 151 Appendix A 154 Appendix B 156 Appendix C 184 7 Appendix D 200 Appendix E 202 Appendix F 204 References 205 8 List of Tables Table 2-1. Descriptive statistics from ANT and random non-ANT players. 58 Table 3-1. The correlation matrix of the cognitive tests used. 94 Table 3-2. Player numbers and ages (in years) for the four games in our analyses after the outlier rejection and the age limits imposed. 99 Table 3-3. The homogeneity of variance tests for each game. 99 Table 3-4. Multiple planned contrasts for each game and age group. 101 Table 4-1. Our HEXACO-60 descriptives with the alternative means of a college aged male sample in parentheses. 124 9 List of Figures Figure 1-2. Two examples of microworlds. 27 Figure 1-3. A snapshot of Marble Madness. 28 Figure 1-4. An example of Sorkin’s maze. 33 Figure 1-5. McPherson’s Space Code (left) and Space Matrix (Right). 36 Figure 2-1. Gameplay within League of Legends. 51 Figure 2-2. Histograms of player ages estimated from usernames from five servers. 58 Figure 2-3. Joint histogram of age estimates extracted from two different sources (N=10,299) with birth years between 1985 and 1999. 59 Figure 2-4. In game metrics for the two categories. 61 Figure 2-5. The valence of interaction rates changes with age. 62 Figure 3-1. The Cattel-Horn-Carrol (CHC) Framework. 79 Figure 3-2. The Verbal Perceptual Rotation (VPR) model. 80 Figure 3-3. The Operation Span task (OSPAN). 91 Figure 3-4. The Rotation Span Task (ROTSPAN). 91 Figure 3-5. The Symmetry Span Task (SymSpan). 92 Figure 3- 6. Cross correlations between variables of interest. 94 Figure 3-7. Age profiles of MMR in four different games. 101 Figure 4-1. The top 6 questions with missing values. 121 Figure 4-2. Missingness across the various questions. 122 Figure 4-3. The distribution of Emotionality in our online sample. 124 Figure 4-4. The distribution of the Maximum Rank Achieved in Hearthstone in our online sample. 124 Figure 4-5. The distribution of Emotionality and LoL Rank in our laboratory sample. 126 10 Figure 4-6. A correlation heatmap of the Emotionality Subtraits and League of Legends Rank. 127 Figure 5-1. A sample before and after restriction. 1XX Figure 5-2. The difference between a longitudinal and a cross-sectional sample 1XX 11 Acknowledgements I am extremely grateful to IGGI and the EPSRC. This work was supported by grant EP/L015846/1 for the Centre for Doctoral Training in Intelligent Games and Game Intelligence (IGGI - http://www.iggi.org.uk/) from the UK Engineering and Physical Sciences Research Council (EPSRC). I would like to thank Dr. Wade, Dr. Cowling and Dr. Gow for their help and guidance throughout these 4 years. Alex deserves a special mention since he carried most of the burden of having me as a student (and believe me that was not easy!). Thanks a lot man, I respect you a lot and I was really lucky to have you as a supervisor. You have literally being amazing every step of the way. I would also like to thank my fellow (IGGI) students for being a joy to be around. Special thanks goes out to Joe Cutting, Matthew Bedder, David Gundry, Simos Gerasimou, Mihail Morosan, Daniel Berio, Nick Sephton, Pete York, Andrei Iacob, Myatt Aung and Sha Li ! I would also like to thank Dr.