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Levelsofcompetitiveandcooperativeplayindyadicgame experience

J.Matias Kivikangas, Simo Järvelä, Niklas Ravaja

Aalto University

Abstract

Dyadic gaming experience was studied in an psychophysiological experiment where the nature of conflict was varied in four different conditions. 41 same-sex dyads were recorded to study how various levels of competition and cooperation would affect their psychophysiological (facial EMG, EDA, and cardiac) activity levels and self-reports of playfulness. It was found that more competitive modes elicit more positive experiences, but that playfulness does not vary in regard to competitiveness. However, differences and conflicting results with preceding similar study might suggest that the variance in competitiveness was not great enough, and therefore some effects might have left unfound.

Introduction

Grown popularity of digital games has led to many formerly strictly game specific concepts and structures pervading life outside games. This gamification phenomenon (McGonigal, 2011) sees games as powerful motivators in various fields ranging from design of everyday products to politics. The underlying theme is that games are approached differently with certain ludic attitude which differs from the typical mindset (Salen & Zimmerman, 2004, p. 80). Playfulness is in this sense a very similar element - our hypothesis is that it is not in the activity itself, or the rules or the features, but in the attitude and approach to the activity or item. In this line of thinking, e.g. certain games or products are more playful than others because they are approached differently (cf. Kallio, Mäyrä, & Kaipainen, 2010). The goal of gamification, therefore, is to apply the same type of playful attitude towards products or services.

Certain features can naturally draw out particular attitudes better than others. Competition is an important part of the motivation for playing games (Lazzaro, 2004; Raney, Smith, & Baker, 2006; Vorderer, Hartmann, & Klimmt, 2003) and an essential factor among common playing mentalities (Kallio,

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Mäyrä, & Kaipainen, 2010). While playful attitudes are difficult to operationalize and therefore to study, the level of competition is well adjustable in many games. Furthermore, previous studies lay a groundwork for comparisons and thus a bigger picture on the subject.

In another study, we compared cooperative and competitive play with two players playing a classic action game (Bomberman), and found significant differences in tonic physiological activity (Kivikangas & Ravaja, 2012a), which was interpreted as: a) participants experienced more positive emotions during the competitive game mode, and b) this effect was much stronger in males than females (to the point that in some indices, there was no difference between game modes in females at all). Arousal and negative affect did not vary significantly between coiperative and competitive game modes. If this is generalizable to other types of games (and perhaps gamified activities), it would have profound implications to design decisions.

However, that study only used two modes of play: cooperative and competitive. As the decision between modes is not binary, we set this new study – not only to test the earlier results in another game type, but also to broaden the view with four different modes varying the level of competitiveness. At the same time we vary the effect of computer players, as it has been shown that the experiences against human and computer players is significantly different (Kivikangas & Ravaja, 2012b; Mandryk, Inkpen, & Calvert, 2006; Ravaja et al., 2006). In one condition, the participants were playing in one team on the same side against one AI team, in other, in two teams on the same side against the two AI teams but competing against each other about the points, in third, both participants were playing against each other with one AI player on their side, and in fourth, the participants played in their own team against each other, without AI teams in the game. The cooperative and competitive modes in Kivikangas and Ravaja (2012a) study correspond to the first and third conditions here, respectively. If we have succeeded in designing the conditions the competitiveness should increase linearly from first to fourth condition, and if we can repeat the results from the earlier study, we would have a strong case to directly draw conclusions on the experinces elicited by competitiveness, and not simply by the particular aspects of the conditions.

Thus, in this experiment we seek to test the following hypotheses:

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H1. The competition level affects the player experience the same way as in previous experiment (Kivikangas & Ravaja, 2012a); that is, competition elicits more positive responses, especially for males, and there is no difference in negative responses or arousal.

H2. The difference between conditions is linear from first to fourth.

In addition, we had aim to answer the research question: assuming the above hypotheses gain support, is the level of competition related to playful attitude?

Methods

Participants

The participants were 100 Finnish university students recruited in 50 dyads. The dyads were always same sex with 29 male and 21 female dyads with age ranging from 18 to 32 (M = 22.9 years). The participants in the same dyad had volunteered for the experiment together so they knew each other and were likely friends. Due to technical difficulties, 9 of the dyads had to be removed from the physiological dataset, which resulted in 82 participants in 41 dyads.

Stimuli

The participants played Hedgewars (http://hedgewars.org), an open-source clone of a popular commercial game Worms by Team 17. Hedgewars is a turn-based artillery game (two-dimensional map and ballistic shooting, see Figure 1), featuring the pink hedgehogs that are controlled in various game modes. The aim of the game is to be the last team on the map, by reducing the health of the other team’s hedgehogs to zero by shooting, or by blowing them to water. The players had 45 seconds per turn to slowly move the hedgehog, choose any one of the various weapons, and shoot by carefully assessing the needed power and angle to guide the ballistic trajectory near the target. The game provides lots of weapons that have various differences in how they behave, but most of them were turned off, so that the more experienced gamers would not have an unfair edge, and to reduce the variation in action durin a turn. Turn order was randomized by default.

Procedure

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The stimuli was run on Kubuntu 11.04 Linux desktop computer and projected to a 150*110 cm white screen with Hitachi CP-X328 LCD video projector with 1024*768 resolution.

The participants arrived in dyads and signed informed consent forms before the experiment begun. They could practice the game while the electrodes were attached to them, after which there was a 5 minute baseline recording. The participants played Hedgewars in four different conditions in randomized order, sitting 1,7m in front of the screen and sharing the same mouse and keyboard as the game controllers. The conditions were: 1. The participants were playing in one team on the same side against one AI team (cooperation). 2. The participants were playing in two teams on the same side against the two AI teams but competing against each other who has the most kills (competition). 3. The participants were playing against each other, both with one AI team on their side (versus, with AI players). 4. The participants were playing in their own team against each other, without AI teams in the game (versus, without AI players).

Before the experiment the participants filled out a background questionnaire. Before and after each condition the participants filled out a series of self-report questionnaires while their psychophysiological data was recorded for the whole duration of the experiment.

Datacollection

Physiologicaldataacquisition

The physiological signals were recorded from participants with the Varioport-B portable recorder systems (Becker Meditec, Karlsruhe, Germany). Facial EMG activity was recorded from the left corrugator supercilii, zygomaticus major, and orbicularis oculi (CS, ZM, and OO) muscle regions as recommended by Tassinary and Cacioppo (2000), using surface Ag/AgCl electrodes with a contact area of 4 mm diameter (Becker Meditec, Karlsruhe, Germany). Electrodes were filled with Synapse conductive electrode cream (Med-Tek/Synapse, Arcadia, CA). The raw EMG signal was sampled at 1024 Hz, amplified, and frequencies below 57 Hz and above 390 Hz were filtered out, using the analog filter built in the Varioport device. The raw signal was rectified and smoothed implementing a linear phase FIR filter using the Kaiser window method (101 coefficients, low-pass cutoff frequency 40 Hz). EMG signals

Next Media – Play Society D2.2.3.2: Article to be submitted to a journal were high pass filtered at 90Hz using 3rd order Buttersworth filter, rectified and smoothed with a 100 ms moving average window.

Electrodermal activity (EDA) was recorded with Varioport 16-bit digital skin conductance an amplifier (input range = 0–70 ʅS) that applied a constant 0.5 V across Ag/AgCl electrodes with a contact area of 4 mm diameter (Becker Meditec), sampling at 32 Hz. Electrodes were filled with TD-246 skin conductance electrode paste (Med Assoc. Inc.) and attached to the middle phalanges of the ring and little fingers of the subject’s left hand after hands were washed with soap and water (the ring and little fingers were used to reduce the interference between gaming and EDA recording). EDA signal was downsampled to 4 Hz and smoothed using Ledalab (V.3.2.5) toolbox for Matlab, and divided into phasic and tonic components using the nonnegative deconvolution method (Benedek & Kaernbach, 2010). These signals were then quantified to number of skin conductance responses (NSCR) and skin conductance level (SCL). In addition, the SCR driver was extracted, but is not reported here.

Electrocardiogram (ECG) was recorded with a modified lead II configuration (electrodes on low rib on the left and the right collar bone), and sampled at 512 Hz. ECG signal was analyzed using the Ecglab toolbox for Matlab (de Carvalho, da Rocha, de Oliveira Nascimento, Neto, & Junqueira, 2002). R-peaks were identified from the original 512Hz series and corrected for ectopic beats. Interbeat interval (IBI) time series was obtained by interpolating with cubic splines at 4 Hz. Square root of the mean squared difference of successive IBIs (RMSSD) and HF component of spectral IBI was extracted for heart rate variability measures.

Acceleration data were integrated over one second and 3-dimensional axes were added together and rectified by taking a square root from the sum of second powers of the axes, to create the Body Movement variable.

Abdominal respiration and electroencephalography were also recorded, but are not reported here.

Behavioralmeasures

Pre-experimentquestionnaires

The pre-experiment questionnaires included the following trait questionnaires or parts of them: Zuckerman-Kuhlman Personality Questionnaire (Zuckerman, Kuhlman, Joireman, & Teta, 1993), Ten- Item Personality Inventory (Gosling, Rentfrow, & Swann, 2003), and Balanced Emotional Empathy Scale

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(Mehrabian, 2000). In addition, the previous experience with artillery games, more popular version of this game called Worms, and this specific game was asked. Behavioral Inhibition System / Behavioral Activation System questionnaire (Carver & White, 1994), an ad hoc questionnaire based on Gaming Mentalities (Kallio et al., 2010), and Playfulness questionnaire (Barnett, 2007) were also employed, but they are not analyzed yet. All questionnaires were used in Finnish.

Three subscales from ZKPQ were employed. Impulsivity and sensation seeking traits of the participants were assessed with the two facet scales of the Impulsive Sensation Seeking scale of the ZKPQ: (a) Impulsivity scale with 5 items (e.g., ‘‘I very seldom spend much time on the details of planning ahead’’) and (b) Sensation Seeking scale with five items (e.g., ‘‘I like doing things just for the thrill of it’’). Sociability was assessed with five items (e.g., “I tend to start conversations in parties”). Each of the items was rated on a 5-point scale, ranging from 1 (very false for me) to 4 (very true for me).

Ten-Item Personality Inventory was used in its entirety. It assesses the Big Five personality traits, Extroversion, Agreeableness, Conscientiousness, Emotional Stability, and Openness to Experience, each with two questions consisting of a pair of adjectives (e.g., “extraverted and enthusiastic” for Extraversion and “critical and quarrelsome” as a reversed item for Agreeableness). The adjectives were assessed on a 7-point likert scale how well they apply to the answerer, ranging from “disagree strongly” to “agree strongly”.

Abbreviated Balanced Emotional Empathy Scale consisted of seven statements (e.g., “I hardly ever cry when watching a very sad movie”) that the participant rated for how well they, on average, apply to him/herself. The agreement was presented with a 9-point scale from +4 to -4, or “very strong agreement” to “very strong disagreement”.

Pre-andpost-conditionquestionnaires

Pre- and post-condition questionnaires included the following state-questionnaires that participants filled after every condition: Self-Assessment Manikins (Bradley & Lang, 1994) and Social Presence module of the Game Experience Questionnaire (de Kort, Ijsselsteijn, & Poels, 2007) with an additional scale from Social Presence Inventory (Biocca & Harms, 2003). In addition we used custom items to assess primary and secondary appraisal, shortened PANAS-X (Watson, Clark, & Tellegen, 1988), and ad hoc items for state assessment based on Playfulness questionnaire Gregariousness, Uninhibitedness, and Comedy (Barnett, 2007), in addition to a separate question “How playful did you feel?”.

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Participants rated their emotional reactions in terms of valence, arousal, and dominance to each of the games using 9-point pictorial scales called Self-Assessment Manikins. The valence scale consists of nine graphic depictions of human faces in expressions ranging from a severe frown (most negative) to a broad smile (most positive). Similarly, for arousal ratings, there are nine graphical characters varying from a state of low visceral agitation to that of high visceral agitation, and nine more characters that vary in size representing dominance.

Social Presence module of the Game Experience Questionnaire, which includes subscales for Empathy (6 items, e.g. “I felt connected to the other”), Behavioral Involvement (6 items, e.g. “What the other did affected what I did”), and Negative Feelings (5 items, e.g. “I felt schadenfreude”). The participants assessed their feelings with 5-point scale from “not at all” to “extremely”. In addition, the perceived comprehension scale from Social Presence Inventory was employed, with both directions (assessed questions such as “My mood affected the mood of the other” for me affecting the other and the other affecting me), with the same 5-point scale.

Dataanalysis

Physiological data was aggregated to obtain one average for each playing period, 240-s baselines were extracted by subtracting 30 seconds from the end of the baseline period, and aggregating the four preceding minutes to one average. Logarithmic transformations were conducted for all physiological signals (play period average + 1, to keep the logarithmic values above zero), both dependent variables and baselines, to normalize the distributions.

Linear Mixed Model was run on SPSS 20 for Windows to create and analyze statistical models (LMM was used due to repeated and dyadic nature of the data).

In LMM for the physiological signals (separately for each signal) as the dependent variables, the baseline value was introduced as a covariate to control the individual differences between participants. According to instructions by Kenny and colleagues (Kenny, Kashy, & Cook, 2006), intraclass correlations of each signal were tested (F-test) for dyad members to check the independence. Of physiological signals, facial EMG, SCL, NSCR, and HF were found nonindependent, and were therefore analyzed further with Dyad as subject variable and Participant × Playing Period as repeated; independent signals, IBI and RMSSD, were analyzed individually with only the Playing Period as a repeated, and Individual as a

Next Media – Play Society D2.2.3.2: Article to be submitted to a journal subject variable. Participants were considered indistinguishable, as for our purposes the two friends in the dyad were completely exchangeable.

The basic models were defined to include Condition (cooperation, competition, versus with AI, versus without AI), Sex, Sex × Condition interaction, and Order of Playing Period as factors, and Previous Experience as another covariate. Although Kenny and colleagues (2006) recommended compound symmetry covariance structure for the residuals, our repeated design (four successive play periods) deviated from the basic dyadic one, and tests revealed that the first-order autoregressive covariance structure - a typical choice due to autoregressive nature of physiological data - provided the smallest Schwarz’s Bayesian Criterion (BIC) values for models where the physiological variable was dependent, it was selected for those models. After testing the basic model, the BIC value was compared with the baseline model, and improving the model was started in a stepwise manner by removing those variables that had the least significant effect, until the improved model was better than the baseline model, and it did not have any variables with non-significant effects left.

For the questionnaires as dependent variables, the models were built in similar manner: baseline was defined as a covariate (where available), Dyad as subject variable and Participant × Playing Period as repeated, compound symmetry as covariance structure. For personality trait tests, a delta variable (play period minus the baseline) of the playfulness scales were computed for a dependent variable instead of raw value, Condition and Segment were defined as factors, and the personality traits were defined as covariates.

Results

Hypothesesͳandʹ For body movement and interbeat interval signals the baseline model remained the best, based on their BIC values (that is, every extra variable was removed from the basic model in the stepwise procedure without the model reaching better fit than the baseline model, as measured by BIC). For OO and CS EMG activity the baseline model had a better fit, although Sex (and for OO, also Condition × Sex interaction) remained significant. In case of HF of heart rate variability, the baseline was the only variable predicting the dependent. For all, the best model is shown in Table 1.

In short, in regard to Hypothesis 1, ZM and OO EMG followed the expected results for Condition, but not for Condition × Sex interaction, where males had a linear increase across Conditions, but females had (a

Next Media – Play Society D2.2.3.2: Article to be submitted to a journal v-form) decrease from 1 to 2 and increase from 2 to 4 (this pattern was repeated as statistically significant in OO, although the final model for OO did not include the interaction). IBI and Body Movement showed conflicting results (see below). In addition, an effect in CS EMG and NSCR in relation to Condition was found. For Hypothesis 2, a linear increase (ZM, OO) or decrease (CS, NSCR) was found, whereas the Body Movement showed a spike in Condition 3 compared to about equal level in other Conditions, and IBI showed a drop in Condition 4, compared to about equal level in other Conditions.

Playfulness Self-reported playfulness was compared between baseline and play periods with a paired-samples t- test, where it was found that each assessment, separate question Playfulness, and Gregarious, Uninhibited, and Comedic Playfulness, was significantly higher in play periods than in baseline, t(313) = 3.21, 4.79, 4.47, and 5.86, respectively, all ps < .001. However, playfulness was not found to be associated with Condition (ps = .45, .36, .65, and .49, respectively), as all scales were predicted solely by the baseline level, Fs(3, ~274) = 31.9, 82.4, 121.3, and 83.2, respectively, all ps < .001.

The above lack of association with playfulness scales and condition was repeated when tested for SAM associations: Gregarious, Comedic, and separate item Playfulness were only predicted (in addition to baselines, reported above) positively by SAM valence, Fs(1,~313) = 30.8, 46.7, and 36.5, all ps < .001, and Uninhibited Playfulness was predicted positively by SAM valence, F(1,303.34) = 3.89, p = .05, and by SAM arousal, F(1,301.81) = 13.35, p < .001.

When Big Five traits were tested for associations with the playfulness scales, it was found that none of the personality traits predicted delta Gregarious and Comedic Playfulness. Delta Uninhibited Playfulness was predicted negatively by Extraversion, F(1,309.22) = 18.31, p < .001, and Agreeableness, F(1,304.95) = 7.13, p = .008, and positively by Conscientiousness, F(1,268.89) = 5.18, p = .024, and Openness to Experience, F(1,285.75) = 5.58, p = .019. and the delta of separate item Playfulness was predicted negatively by Agreeableness, F(1,253.11) = 6.04, p = .015 and positively by Openness to Experience, F(1,216.12) = 5.46, p = .020. Due to this result, an additional analysis was run, and it was found that both Extraversion and Agreeableness positively predicted baseline Uninhibited Playfulness, ps < .001.

For social presence analyses, delta Gregarious, delta Uninhibited, and delta separate item Playfulness (but not delta Comedic) were all positively predicted by GEQSP Empathy, Fs(1,~314) = 5.31, 4.85, and F(1, 310) = 4.93, ps = .022, .028, and .027, respectively, but not by any other social presence scale.

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Discussion

Hypothesesͳandʹ It was found that positive affect (indexed by ZM and OO EMG) did increase when the purported competitiveness increased, as suggested in our Hypothesis 1, and in linear manner, as suggested by Hypothesis 2. However, the sex difference found by Kivikangas and Ravaja (2012a) failed to reproduce, as females showed higher activity in condition 3 (and 4) than in 1 (and the lowest in 2). Negative affect (indexed by CS EMG) decreased in a linear fashion from 1 to 4, supporting Hypothesis 2 but not Hypothesis 1, as Kivikangas and Ravaja did not report difference in negative affect.

SCL, the main index for arousal, showed no difference between conditions, as expected. However, NSCR – another index for arousal, although also associated with orienting responses (Tassinary & Cacioppo, 2000) – decreased in linear fashion from condition 1 to 4. It is likely that the huge effect of baseline for SCL might mask any weak differences between conditions; therefore, it could be argued that arousal decreased across the conditions, a result conflicting with Hypothesis 1.

To explain the inconsistencies, we would turn the attention on the differences in the stimulus games. As games, Hedgewars, which we used in this experiment, and Bomberman, used by Kivikangas and Ravaja (2012a), while not by any means identical, are quite similar in many ways. They both use simplistic cartoonish and colorful game graphics, and portray very positive atmosphere upheld by happy music and sounds. They are quite simple and gamelike games, i.e. their mechanics are very apparent and the gameplay is instantly geared towards winning within the framework defined by the rules (compared to the big triple-A releases, which consistently aim at cinematic experiences with engaging stories). Naturally they also both include clear conflict structure and provide free assignment of teams, AI opponents and different game modes – a big reason to choose them. Also, it can be argued that they both are played with rather similar approach and in similar situations. They are easily approachable and social games, that would presumably be played as social low commitment activity with friends (cf. Kallio, Mäyrä, & Kaipainen, 2010). However, despite the very similar conflict structures in the games, it can be argued that the competition is framed differently in them, with Bomberman being more aimed at winning, while in Hedgewars there is a distinct comic flavour even in failure. This difference can possibly lead to different gaming experience despite the structural similarities – i.e., competition in Bomberman feels more like competition, and competition in Hedgewars feels more like a context for having fun together. This would be supported by the fact that arousal (and negative affect) actually decreased the

Next Media – Play Society D2.2.3.2: Article to be submitted to a journal more competitive the condition was, indicating that the participants got more relaxed, not less, as competitive attitudes would dictate.

Playfulattitudes Tests showed that the conditions were experienced as playful (and that playfulness was higher when self-reported positive affect was higher) with all the self-report indices, but that they did not differ significantly from each other. If our reasoning above is true about the nature of Hedgewars as a stimulus, it would show exactly this: that although the game is playful and fun, it is equally playful or fun in all the conditions. However, not all relevant analyses were done at the moment of writing this, so further analyses might reveal something deviating from this.

Examining the personality traits showed that uninhibitedness – a specific aspect of playfulness – was associated negatively with differenceto baseline in extraversion, agreeableness, and positively with difference to baseline in conscientiousness and openness to experience, agreeableness and openness to experience also being associated with direct personal assessment of playfulness in similar fashion. Simplified, this means that less extroverted and agreeable (a trait relating to accommodating with social situations) and more open (to new experiences) people actually felt more increase in uninhibitedness during the game than those high in the traits – or even more simplified, that shy but not fearful people felt more relaxed and less social pressure, and also more playful. This should be interpreted as a sign that the game really was playful, to the point that it helped more reserved people into playful and less socially inhibited mood.

It was also found that different aspects of playfulness were associated with Social Presence Empathy scale, which concerns with and sharing positive feelings with the other player. It suggests that the participants had fun because they were playing together, not due to game per se, as playful as it might be in itself.

Limitationsandfuturestudies In hindsight, it might be that the selected game was too playful to create variation that could have been captured with many of our measures. Although the basic result – more positive affect in more competitive situations – gained support, a shadow of doubt is cast by other results unable to confirm the difference between employed levels of competitiveness. The relatively impressive number of participants in a psychophysiological study suggests that the found differences were not due to pure

Next Media – Play Society D2.2.3.2: Article to be submitted to a journal chance, but the possibility that other differences than specifically competitiveness might have contributed remain. This should naturally be tested with further studies with similar manipulations, but with different stimulus (or better yet, comparing Hedgewars, Bomberman, and a third game). Another option would be to test whether extrinsic motivation (such as monetary rewards) would change the competitiveness levels, and thus the results.

References

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Table 1.

Linear Mixed Models for Physiological Dependents by Condition

Estimate M for conditions

Variable source 1 2 3 4 SE df F

Nonindependent Members

Zygomaticus Major EMG activity (ln[µV+1])

Condition 1.496 1.512 1.682 1.694 0.061 3,243.23 16.23***

Sex . . . . . 1,49.00 7.68**

Condition 1.570 1.665 1.888 1.886 0.076

× Sex 1.423 1.358 1.475 1.501 0.095 3,246.30 5.43**

Order of Condition . . . . . 3,247.32 9.92***

Baseline 0.654 0.122 1,294.00 28.67***

Corrugator Supercilii EMG activity (ln[µV+1])

Condition 1.055 1.060 1.021 0.991 0.027 3,243.47 7.13***

Order of Condition . . . . . 3,248.74 6.14***

Baseline 0.421 0.064 1,279.00 43.81***

Orbicularis Oculi EMG activity (ln[µV+1])

Condition 1.634 1.670 1.841 1.848 0.050 3,243.39 27.68***

Order of Condition . . . . . 3,247.55 7.87***

Baseline 0.585 0.096 1,320.07 36.89***

Skin Conductance Level (ln[µS+1])

Order of Condition . . . . . 3,247.99 8.98***

Baseline 0.865 0.027 1,268.55 1054.06***

Number of Skin Conductance Responses (ln[n])

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Condition 3.663 3.522 3.535 3.452 0.060 3,221.23 4.24**

Order of Condition . . . . . 3,230.36 8.83***

Previous Experience -0.141 0.041 1,119.43 11.69**

Baseline 0.204 0.043 1,159.24 22.133***

Body Movement

Condition 0.013 0.012 0.015 0.013 0.001 3,132.50 3.00*

Order of Condition . . . . . 3,118.34 2.44

Baseline 0.191 0.071 1,65.27 7.19**

High Frequency Band of Heart Rate Variability

Baseline 0.164 0.044 1,156.08 13.87***

Independent Members

Interbeat Interval (ln(ms))

Condition 6.709 6.708 6.702 6.685 0.008 3,228.61 11.48***

Order of Condition . . . . . 3,239.92 3.24**

Baseline 0.752 0.061 1,82.77 153.70***

RMSSD

Sex . . . . . 1,89.35 4.32*

Order of Condition . . . . . 3,231.39 2.68*

Baseline 0.626 0.069 1,90.43 81.91***

Note. The estimated means are only shown for Condition, and covariates Baseline, and Previous Experience. For Condition × Sex interaction, the first row represents male and the second row female sex. Estimate for Baseline and Previous Experience is displayed in the form where other variables are kept at condition 1. Intercept is left out as uninformative.

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* p < .05, ** p < .01, *** p < .001.

Figure 1. Screenshot of a typical situation in the game Hedgewars.