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Levels of competitive and cooperative play in dyadic game
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.
Data collection
Physiological data acquisition
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
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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 S) 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.
Behavioral measures
Pre-experiment questionnaires
The pre-experiment questionnaires included the following trait questionnaires or parts of them:
Zuckerman-Kuhlman Personality Questionnaire (Zuckerman, Kuhlman, Joireman, & Teta, 1993), TenItem 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- and post-condition questionnaires
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.
Data analysis
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
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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
Hypotheses
and
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
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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 ttest, 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
Hypotheses
and
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
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more competitive the condition was, indicating that the participants got more relaxed, not less, as
competitive attitudes would dictate.
Playful attitudes
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.
Limitations and future studies
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
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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.
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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
Condition
1.570
1.665
1.888
1.886
0.076
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***
× Sex
7.68**
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***
.
3,247.99 8.98***
0.027
1,268.55 1054.06***
Skin Conductance Level (ln[µS+1])
Order of Condition
.
Baseline
0.865
.
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
0.043
1,159.24 22.133***
0.204
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
0.044
1,156.08 13.87***
7.19**
High Frequency Band of Heart Rate Variability
Baseline
0.164
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***
4.32*
RMSSD
Sex
.
.
.
.
.
1,89.35
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.