psychophysiological responses to online poker game

IADIS International Conference Game and Entertainment Technologies 2009

PSYCHOPHYSIOLOGICAL RESPONSES TO ONLINE POKER GAME Kari Kallinen1, Mikko Salminen1, Niklas Ravaja1, Kliment Yanev2 1

Helsinki School of Economics, 2University of Cologne

ABSTRACT The goal of the present study was to examine the psychophysiological and other responses to online poker game and to compare the responses during players own versus opponents’ turn. A total of 54 subjects played Texas Hold’em in groups of four players. Psychophysiological responses (e.g., heart rate, facial muscle activity and electrodermal activity), as well as subjective responses (e.g., enjoyment) were measured. The most effective game events in eliciting psychophysiological responses were blinds, showing cards after a player went ALL-IN, and winning. The beginning of a round (setting blinds) elicited negative emotion and higher arousal; showing cards after a player went ALL-IN elicited positive emotion, higher arousal and higher attention; winning elicited positive emotion and decrease in attention. In regard to responses between player vs. opponents’ turn, we found that in general the important player activities (e.g., beginning of a round, result of the round, call, all-in, show) elicited higher responses during players own turn, whereas the important activities by opponents (e.g., bet, check, fold, set) seem to elicit higher responses in player during the opponents’ turn. The responses were strongly moderated by trait personality (behavioral inhibition system [BIS] and behavioral activation system [BAS]. Thus, the results also suggest that personality measures as potential moderators of game behavior and experience should be taken into account in future studies on responses to poker games. KEYWORDS Online Poker, Psychophysiological Responses, Personality.

1. INTRODUCTION Poker's has become very popular due to exposure on television, on the Internet and in popular literature. The ability to play cheaply and anonymously, to try out games and organize large tournaments online has been credited as a cause of the increase in popularity of poker. Texas hold 'em is currently the most popular form of poker. It is a community card game, meaning that some cards are dealt face up in the middle of the table and shared by all players. Each player is dealt two pocket or hole cards which they consider with the five community cards to make the best possible five-card hand. Playing poker game involves largely social and psychological information. Players aim to hide (e.g., keep a “poker face”) or bluff their own responses while on the other hand they also try to “read” opponents actions and responses (e.g., nervousness). In face-to-face game the implicit information is in some extent available for players, whereas online card room interfaces do not support the subtle communication between players that is integral to the psychological aspect of the game. Therefore, people may be more spontaneous and visible in their responses in online poker than in face-to-face poker, thus making it possible to better identify the most important events in the game by examining the user responses (e.g., heart rate, sweating and facial movements). We conducted an explorative study on online poker to study what game events elicit highest psychophysiological responses. The assumption here is that those are the most important game events for players. The main question addressed by this study was whether there are constant patterns in psychophysiology during the course of a competitive social game. In the present paper, we will focus on selfreport mood and psychophysiological responses. We will also present some effects of the moderating factors, such as personality.

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2. METHOD 2.1 Subjects Fifty-four subjects with varying fields of profession participated in to the study. The subjects were 46 men and 8 women ranging from 18 to 41 (M= 30.0) years of age. Most of the subjects were university students or researchers. In addition there was some IT specialists, product managers, sales managers etc. From the total of 54 subjects, 25 (18 men and 7 women ranging in the age of 20 to 38, M=30) played the game with the Psychophysiological sensors attached to them.

2.2 The Game The present experiment used Open Source poker software PokerTH (see Figure 1). Program was modified so that it read a text file containing either a given set of cards or the command to deal the cards randomly. The program created a log file containing information on player cards, table cards, stakes, and timestamps.

Figure 1. Poker TH

The game session consisted of 31 rounds. Fifteen of the rounds were played with random cards and 16 rounds were played with predetermined cards. The 12 manipulation varied the type of winning cards (pair, two pairs, three of a kind, straight) and the game phase in which the winning hand was built (preflop, flop, turn, river). Winning probabilities were calculated prior to ensure that the overall winning probabilities are as equal as possible. This approach ensured that certain situations occurred during the game (e.g. winner gets winning cards on Pre-Flop, Flop, Turn or River).

2.3 Measures 2.3.1 Game Questionnaire The players’ game experience was assessed using a nine item self-report questionnaire (i.e., “I enjoyed playing the game”, “Playing the game frustrated me”, “Playing the game bored me”, “I was excited when I had good cards”, “I was excited when I bluffed”, I was happy about rounds I won”, “I was annoyed by losing a round”, “My arousal increased with the height of my bets”. All items were rated using a 5-point Likert scale from 1 (I strongly disagree) to 5 (I strongly agree).

2.3.2 BIS/BAS Sensitivities Dispositional BIS and BAS sensitivities of the participants were measured with the BIS/BAS scales (Carver & White, 1994), a 20-item self-administered questionnaire. The BIS scale is comprised of 7 items (e.g., “I feel pretty worried or upset when I think or know somebody is angry at me”). The BAS scale is comprised of

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three subscales: Drive (4 items; e.g., “I go out of my way to get things I want”), Reward Responsiveness (5 items; e.g., “When I get something I want, I feel excited and energized”), and Fun Seeking (4 items; e.g., “I crave excitement and new sensations). Each of the items was rated on a 4-point scale, ranging from 1 (very false with me) to 4 (very true for me). The psychometric properties of the instrument have been shown to be acceptable (Carver & White, 1994).

2.3.3 Physiological Data Collection and Data Analysis A mobile physiological data acquisition system (Varioport-B, Becker Meditec, Karlsruhe, Germany) was used to record facial EMG (2 channels), ECG, and EDA. EDA (skin conductance level [SCL]), facial EMG (ZM and CS), and ECG (in terms of heart rate [HR]) responses to the following game events were analyzed: ALL-IN: A player put all his/her money into the pot BET: A player raises the money in the pot BLIND: A player set his/her blind CALL: A player calls the last bet CHECK: A player does not want to raise FOLD: A player folds HAS: A player shows his/her cards after a player went ALL-IN SET: A player raises after somebody BETS before (instead of just calling) SHOW: A player shows his/her cards after everybody either checked or folded. WIN: A player wins The psychophysiological data consisted of a 2 second local baseline value before the particular game event and 6 seconds event data value from the beginning of the game event. Delta values were calculated (event data minus local baseline) and analyzed using The General Linear Model (GLM) Univariate (for selfreport measures) and Repeated Measures (for psychophysiological data) procedure in SPSS.

2.4 Procedure Four subjects were recruited for each playing session and placed each in own, quiet room. Before the game, the subjects were given a Power Point introduction to Hold’Em Poker to read. Each participant also received a printed handout explaining the relevant poker terminology and the course of the game as well as the interface of the specific poker software we used. After the short introduction to Texas Hold’Em Poker and the user interface the psychophysiological sensors were attached to those people that played with sensors. In the game the subjects started with 5.000$. Subjects were informed that each game session consist of 31 rounds (approx. 45 min) and the winner is the player that has the largest amount of money at the end of the game. Given that it was not possible to give money at the present study, subjects were also told that the winner will receive 4 movie tickets (circa 8.5 euro of value each), the second 3 tickets, the third 2 ticket and the looser 1 ticket. After the game subjects administered a questionnaire about the playing experience, background information (e.g., age, gender etc.), and personality. Then psychophysiological sensors were detached and participants were thanked and rewarded.

3. RESULTS 3.1 Self-report Mood As illustrated in table 1, generally participant enjoyed playing the game (M=4.17 in 1 to 5 point scale) and lacked the sense of frustration (M=2.22) and boredom (M=1.74). Participants were most likely to be excited when they had good cards (M=4.19), happy when they won a round but angry when they lose a round (M=4.50).

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ISBN: 978-972-8924-85-0 © 2009 IADIS

Table 1. Self-report Ratings for Player Experience

The General Linear Model (GLM) Univariate procedure in SPSS revealed a significant main effect for sensitivity to Behavioral activation system (BAS) in predicting angriness when lost a round, F(1,44) = 4.64, p=.037. Further analysis showed that this was especially true for BAS reward responsiveness (a subscale of BAS), F(1,44) = 9.35, p=.004. Subjects scoring high on BAS-RR reported higher level of angriness when lose around than subjects scoring low on Bas-RR (Ms = 4.84 and 4.30). The analysis showed also a significant main effect for gender in predicting frustration when playing the game, F(1,52)=4.40, p=.041. Women were more frustrated playing the game than men (Ms = 2.88 and 2.11). The analysis revealed also a significant main effect for gender in predicting happiness when winning a round, F(1,52) = 5.09, p=.028. Men were happier than women when they won around (Ms = 3.80 and 2.88).

3.2 Psychophysiological Responses During Game Events The psychophysiological responses during different game events are illustrated in figure 2. As shown in topleft panel, skin conductance level were highest during blinds (i.e., in the beginning of a round), when player show his/her cards after player went ALL-IN, and when player won. As also illustrated in the top-left panel, SCL was lowest during All-in. Top-right panel shows that ZM activity was highest when player won, showed his/her cards after a player went ALL-IN, and when player raised money in the pot. In contrast ZM activity was the lowest during blinds. Top-right panel also shows that all game events elicited positive responses in players with an exception of setting the blinds. As illustrated in bottom-left panel, all events except setting blinds (i.e., the beginning of a round) and showing cards after player went ALL-IN elicited decrease in CS activity. CS activity was lowest during Allin and highest during blinds. As illustrated in bottom-right panel, decrease in HR was elicited when player showed his/her cards after a player went ALL-IN and when a player put all his/her money into the pot. In contrast, HR increased when a player won. In sum, the most effective game events in eliciting psychophysiological responses were blinds, showing cards after a player went ALL-IN, and winning. The beginning of a round (setting blinds) elicited negative emotion (increase in CS) and higher arousal (increase in SCL); showing cards after a player went ALL-IN elicited positive emotion (increase in ZM), higher arousal (increase in SCL) and higher attention (decrease in HR); winning elicited positive emotion (increase in ZM) and decrease in attention (increase in HR).

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Mean delta ZM activity

Mean delta SCL

0,40

0,30

0,20

0,10

0,00

All-in

Bet

Blind

Call

Check

Fold

Has

Set

Show

3,00

2,00

1,00

0,00

Win

All-in

Bet

Blind

Call

Mean delta CS activity

Game event

Check

Fold

Has

Set

Show

Win

Game event

4,00

2,00

0,00

-2,00

-4,00 All-in

Bet

Blind

Call

Check

Fold

Has

Set

Show

Win

Game event

Mean delta HR

5,00

0,00

-5,00

-10,00 All-in

Bet

Blind

Call

Check

Fold

Has

Set

Show

Win

Game event

Figure 2. Mean Delta Skin Conductance Level (1st Panel), ZM Activity (2nd Panel), CS Activity (3rd Panel), and HR (4rd Panel) During Game Events

3.3 The Psychophysiological “Discourse” During Game: Player’s Own vs. Opponent’s Responses Separate GLM repeated measures analyses for each game event were conducted to compare the responses during player’s own and during opponent’s actions.

3.3.1 The Beginning of a Round: Setting Blinds vs. Watching the Opponents Setting Blinds The General Linear Model (GLM) Repeated Measures procedure in SPSS, with blind setter (i.e., player vs. opponent) as within-subjects factor revealed a significant main effect for blind setter in predicting delta SCL, F(1,8) = 7.64, p=.025. Setting a blind elicited higher delta SCL (M=0.26) than watching the opponents setting blinds (M=-0.03).

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ISBN: 978-972-8924-85-0 © 2009 IADIS

3.3.2 Check vs. Watching Opponent Check The General Linear Model (GLM) Repeated Measures procedure in SPSS, revealed a significant interaction between Checker and BAS-RR in predicting delta SCL, F(1,16) = 4.66, p=.046. Delta SCL was higher during opponent’s check than during players own check among high BAS-RR scorers (Ms = 0.01 and -0.02), whereas among low BAS-RR scorers there was no difference between opponent and own check (Ms = -0.02 and -0.02). In this model (controlling BAS-RR) there was also a main effect for checker in predicting delta SCL, F(1,16) = 5.00, p=.040. Delta SCL was higher during opponent check (M= -0.01) than during player’s own check (M=-0.02). The analysis also showed a significant interaction between Checker and BIS in predicting delta ZM activity, F(1,16) = 7.55, p=.014. Delta ZM activity was higher during player’s own check than during opponents’ check especially among high BIS scorers (for high BIS scorers, Ms = 1.66 and -1.48; for low BIS scorers, Ms = 0.14 and -0.29). In this model (controlling for BIS) there was also a main effect for checker in predicting delta ZM activity, F (1,16) = 9.08, p=.008. Delta ZM activity was higher during player’s own check (M = 0.73) than during opponents’ check (M = -0.75).

3.3.3 Call vs. Watching Opponent Call The General Linear Model (GLM) Repeated Measures procedure in SPSS revealed a significant interaction between Caller and BAS-RR in predicting delta SCL, F(1,16) = 8.58, p=.010. Delta SCL was higher during players own call than during opponents’ call among high BAS-RR scorers (Ms = 0.03 and 0.00), whereas among low BAS-RR scorers there was no difference between own and opponent call (Ms = -0.01 and -0.00). In this model (controlling BAS-RR) there was also a main effect for caller in predicting delta SCL, F(1,16) = 6.82, p=.019. Delta SCL was higher during player’s own call (M= 0.02) than during opponents’ call (M=-0.00).

3.3.4 Bet vs. Watching Opponent Bet The General Linear Model (GLM) Repeated Measures procedure in SPSS revealed a significant interaction between Bettor and BIS in predicting delta HR, F(1,15) = 7.92, p = .013. Delta HR was lower during player bet (M= -2.22) than during opponent bet (M= -0.5) among high BIS scorers, whereas among low BIS scorers the opposite was true, i.e., Delta HR was lower during opponent bet (M=-0.19) than during players own bet (M=2.9). In this model (controlling BIS) there was also a main effect for bettor in predicting delta HR, F(1,15) = 6.91, p=.019. Delta HR was lower during opponent bet (M= -0.14) than during player’s own bet (M=1.09).

3.3.5 Fold vs. Watching Opponent Fold The General Linear Model (GLM) Repeated Measures procedure in SPSS revealed a significant interaction between the Performer of Fold and BIS in predicting delta HR, F(1,15) = 6.02, p = .027. Delta HR was lower during player’s own fold (M= -2.24) than during opponents’ fold (M= -0.86) among high BIS scorers, whereas among low BIS scorers the opposite was true, i.e., Delta HR was lower during opponent fold (M=0.35) than during players own fold (M=0.68). In this model (controlling BIS) there was also a main effect for folder in predicting delta HR, F(1,15) = 5.79, p=.029. Delta HR was lower during opponent fold (M= -0.56) than during player’s own fold (M= 0.52).

3.3.6 All-in vs. Watching Opponent All-in The General Linear Model (GLM) Repeated Measures procedure in SPSS revealed a significant interaction between the Performer of All-in and BAS in predicting delta HR, F(1,7) = 6.83, p=.035. Delta HR was lower during player All-in (M= -1.72) than during opponent All-in (M= 2.78) among high BAS scorers, whereas among low BAS scorers there was no difference in delta HR (Ms = 0.62 and 0.43). In this model (controlling BAS) there was also a main effect for Performer of All-in in predicting delta HR, F(1,7) = 8.15, p=.025. Delta HR was lower during player All-in (M= -0.68) than during opponent All-in (M= 1.74).

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3.3.7 Showing Own Cards After Another Player Went ALL-IN vs. Watching Opponent Showing His/Her Cards after Another Player Went ALL-IN The analysis revealed no significant main effects or interactions for the player (self vs. opponents) showing his/her cards after another player went ALL-IN in predicting EDA, EMG, or HR.

3.3.8 Raising Money after Somebody Bet before vs. Watching Opponents’ Raising Money after Somebody Bet before The General Linear Model (GLM) Repeated Measures procedure in SPSS revealed a significant main effect for the player (self vs. other) that raises after somebody bets before (instead of just calling) in predicting delta ZM activity, F(1,18) = 4,98, p=.39. Raising money after somebody bet before elicited higher delta ZM activity than watching opponents’ raising money after somebody bet before (Ms=0.59 and -1.05). The analysis also showed a significant main effect for player (self vs. other) that raises after somebody bet before in predicting delta HR activity, F(1,18) = 11.31, p=.003. Watching opponents’ raising after somebody bets before elicited lower delta HR than rising self after somebody bets before (Ms = -3.49 and 0.52).

3.3.9 Showing His/Her Cards after Everybody either Checked or Folded vs. Watching Others’ Show their Cards after Everybody either Checked or Folded (SHOW) The General Linear Model (GLM) Repeated Measures procedure in SPSS revealed a significant interaction between The Performer of SHOW and BAS in predicting delta SCL, F(1,14) = 5.49, p=.034. Showing cards after everybody either checked or folded elicited higher delta SCL than watching others’ show their cards after everybody either checked or folded among low BAS scorers (Ms = 0.054 and 0.022), whereas the opposite was true for high BAS scorers (Ms = 0.004 and 0.02). In this model there was also a significant main effect for the performer of SHOW in predicting delta SCL, F(1,14) = 5.10, p=.040. Showing his/her cards after everybody either checked or folded elicited higher delta SCL than watching others’ show their cards after everybody either checked or folded (Ms = 0.029 and 0.021).

3.3.10 The End of a Round: Winning vs. Losing The General Linear Model (GLM) Repeated Measures procedure in SPSS, with game result (i.e., losing, winning) as within-subjects factor revealed a significant interaction between BAS and Game Result in predicting delta SCL, F(1,15) = 6.47, p = .022. Losing elicited higher delta SCL (M=0.17) than winning among high BAS scorers (M=0.08), whereas the opposite was true for low BAS scorers (Ms=0.05 and 0.61). In this model (controlling BAS) there was also a main effect for Game Result in predicting delta SCL, F(1,15) = 7.39, p=.016. Delta SCL was higher during winning (M=0.33) than during losing (M=0.11).

3.3.11 Summary In sum, as illustrated in table 2, when the personality dimensions of BIS and BAS were taken into account, the comparison of responses during players own vs. opponents’ actions showed several differences in arousal, attention, and emotion.

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Table 2. Summary of Results Arousal (SCL) Self Opponent

Game event All‐in Main effect Mod.effect

Response Attention (HR) Self Opponent ↑ ↓ High BAS ↑

‐

‐

Bet Main effect Mod.effect

‐

‐

↓ High BIS ↑

Blind Main effect Mod.effect

↑

↓ ↓

Call Main effect Mod.effect Check Main effect Mod.effect

↑ High BASRR↑ ↓

↑ High BASRR↑

Positive emotion (ZM and CS) Self Opponent ‐

‐

↑ Low BIS ↑

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

‐

↑ High and low BIS↑

↓

‐

Fold Main effect Mod.effect

‐

‐

↓ High BIS ↑

↑ Low BIS ↑

‐

‐

Has Main effect Mod.effect

‐

‐

‐

‐

‐

‐

Set Main effect Mod.effect

‐

‐

↓

↑

↑

↓

Show Main effect Mod.effect

↑ Low BAS ↑

↓ High BAS ↑

‐

‐

‐

‐

Win Main effect Mod.effect

↑ Low BAS ↑

↓ High BAS ↑

‐

‐

‐

‐

4. DISCUSSION AND CONCLUSIONS The aim of the present study was to examine the psychophysiological and other responses to online poker game and to compare the responses during players own versus opponents’ turn. In general, players reported enjoyment (and lack of frustration and boredom) of the game, even though some personality and gender differences in responses were observed. For example, women were a little more frustrated in playing than men. This is possible due the fact that most of the Poker players are men and generally know the game better. Given the explorative nature of the study, we had not precise hypotheses about the results (there are scarcity of previous studies on psychophysiological responses to online poker). Nevertheless, the results are of importance since they showed that psychophysiological responses differed in terms of specific game events. In sum, the most effective game events in eliciting psychophysiological responses were blinds, showing cards after a player went ALL-IN, and winning. The beginning of a round (setting blinds) elicited negative emotion (increase in CS) and higher arousal (increase in SCL). It is most likely that these responses reflect an orienting response to a new round. We also found that showing cards after a player went ALL-IN elicited positive emotion (increase in ZM), higher arousal (increase in SCL) and higher attention (decrease in

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HR). These responses are logical given the “climax” nature of the aforementioned game event. It was also found that winning elicited positive emotion (increase in ZM) and decrease in attention (increase in HR). It is well known that people like winning and feel positive about it. The decrease in attention after winning can be explained in terms of resolution of tension and a lack of focus of attention in the end of a round (it takes some time until the next round starts). The most significant effects in the present study were observed as differences in arousal, attention and emotion between player vs. opponents’ turn. Generally, the important player own activities (e.g., beginning of a round, result of the round, call, all-in, show) elicited higher responses during players own turn, whereas the important activities by opponents (e.g., bet, check, fold, set) seem to elicit higher responses in player during the opponents’ turn. The result nicely demonstrates the sensitivity of the psychophysiological system in the course of the turn taking in poker game. It also suggest that people are sensitive both to their own and players actions (recall that in face-to-face poker game players aim to scan psychologically meaningful cues from their opponents, while controlling their own responses). We also found that the responses were strongly moderated by behavioral inhibition system (BIS) and behavioral activation system (BAS) sensitivities. Thus, the results suggest that personality measures as potential moderators of game behavior and experience should be taken into account in future studies on responses to poker games.

ACKNOWLEDGEMENT This study was supported by the European Community, IST project “Psychologically augmented social interaction over networks” (IST-27654).

LITERATURE Billings, D., Papp, D., Schaeffer, J., & Szafron, D. (1998). Opponent Modeling in Poker. In Proceedings of AAAI-98. Erev, I. & Roth, A.E. (1998). Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria. The American Economic Review, 88, 848-881. Golder, S. A., & Donath, J. (2004). Hiding and revealing in online poker games. In Proceedings of the 2004 ACM conference on Computer supported cooperative work (pp. 370-373). Chicago, Illinois, USA: ACM Press. Yun, M., Lee, J., Lee, H., & Cho, S. (2005). Classification of Bluffing Behavior and Affective Attitude from Prefrontal Surface Encephalogram During On-Line Game. In D. Zhang and A.K. Jain (Eds.): ICB 2006, LNCS 3832, pp. 706 – 712.

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