arousal and cognitive load in texas hold 'em poker

Arousal and cognitive load in Texas Hold ‘em poker

Psych 499C Honours Thesis

January, 2012

Student: Brendan Sheehan Faculty Advisor: Dr. Michael J. Dixon

University of Waterloo, Department of Psychology

Running Head: AROUSAL AND COGNITIVE LOAD IN TEXAS HOLD EM POKER 1

Abstract

Texas Hold ‘em poker is one of the most popular gambling games today. A previous

study from our lab (Lee & Dixon) investigated whether the action of bluffing could be detected

overtly or covertly. Their results were unexpected and suggested cognitive load plays a crucial

role in the interpretation of arousal changes measured during strong hands, weak hands and bluff

hands. We replicated this study and added a subjective rating of arousal as a new measure to

assess their claims concerning cognitive load. The latter involved a baseline-mental arithmetic

task which used heart rate variability (HRV). Using the baseline-mental arithmetic task we found

a specific HRV variable, SDNN, was most sensitive to cognitive load. Applied to our study we

found no significant differences in SDNN between the three hand types – a finding which fails to

support Lee and Dixon’s cognitive load hypothesis. Importantly, we did replicate their HR

findings and showed that bluffing leads to as great an increase in arousal as a strong hand. We

contend that bluffing leads to high arousal. Given the strong theoretical link between arousal,

reinforcement and problem gambling, our findings may explain the popularity of this game, but

also sound a cautionary warning about a key game feature that could lead to gambling problems.

Key Terms: Texas Hold ‘em poker, Arousal, Cognitive Load, Heart Rate Variability

AROUSAL AND COGNITIVE LOAD IN TEXAS HOLD EM POKER 2

AROUSAL AND COGNITIVE LOAD IN TEXAS HOLD EM POKER

Texas Hold ‘em is one variant of the family of card and betting games collectively known

as poker. Texas Hold ‘em is unique in that gameplay involves both cards known only to the

player as well as a set of communal cards which all players can act upon. In addition, the skill of

the player has at least some effect on the outcome of the game, as opposed to betting games of

pure chance such as roulette or slot machines (Shead, Hodgins, & Scharf, 2008). The game is

composed of four stages of play. First, each player is dealt two cards face down. Next, three

communal cards are dealt which all players can utilize. In the third and fourth round each, a

single communal card is dealt. As the game progresses the player has an increasing pool of

available cards from which to assemble the strongest possible combination of five cards (a hand).

The strength of the player’s assembled hand is in relation to a predefined order of hands,

standard throughout most variations of poker. After each of the four rounds of play, the player

must mentally estimate the probability of their hand winning. They must also approximate the

relative strength of their opponent’s hands. Based on these two mental estimates the player has

three options at each stage: decline further play (to fold), continue playing at the current level of

investment (to check) or, raise the level of investment (to raise). Each player must indicate their

decision in a predefined order (e.g. clockwise around the playing surface). If a player chooses to

raise, other players have the option to fold, to match the raise (to call) or to re-raise. Crucially,

the intensity with which a player bets does not necessarily correlate with the objective strength of

their hand. A player may decide to play a weak hand as if it were a strong one, in the hopes of

deceiving his or her opponents into folding (to bluff). This strategy is made possible because of

the two confidential player-dealt cards. Bluffing adds an extra interpersonal dynamic to the


game, as well as a considerable amount of excitement, as players must attempt to deceive their

opponents into believing their hand has a high probability of winning.

Texas Hold ‘em poker has grown immensely in popularity over the past decade. There are

over 60-80 million players in the US alone, a figure that continues to rise (The Economist, 2007).

Closer to home, over 20% of Ontarians play poker for money, 16% of whom play more than

once a week (RGC, 2006). Yet despite this tremendous, and growing, interest in Texas Hold ‘em

little is understood about the allure of the game. There is a dearth of empirical research

investigating what the attraction to Texas Hold ‘em is or which mechanisms lie behind its

addictive potentiality (Hopley & Nicki, 2010). We believe physiologic arousal may be involved

in both of these aspects of the game.

Arousal is a psychophysiological process linking signals from the environment with

somatic and physical state alterations, which serve to increase one’s ability to act upon their

environment. Fowles (1980) suggests that certain stimuli form links with specific mentations,

which ultimately bring about somatic and kinetic reactions (Fowles, 1980). These stimuli act

upon areas of the brain, including the reticular activating system (Steriade, 1996), causing acute

alterations in cardiovascular, autonomic nervous system and endocrine function within the body.

The net effect of processing these external signals is ultimately an increased ability of the

individual to act upon their environment in an adaptive manner (Woody & Szechtman, 2011).

As a concept, arousal appears throughout a considerable portion of the gambling

literature. Various mediums of gambling activity have been linked to alterations in physiological

arousal including: slot machines (Brown, Rodda, & Phillips, 2004; Dixon, Harrigan, Sandhu,

Collins, & Fugelsang, 2010), video poker machines (Leary & Dickerson, 1985), video lottery


terminals (Ladouceur, Sévigny, Blaszczynski, O'Connor, & Lavoie, 2003) and horse racing

(Cocco, Sharpe, & Blaszczynski, 1995). In addition, arousal features prominently throughout

problem gambling research. Blaszczynski and Nower (2002) included arousal in their influential

pathways model of problem gambling. They postulated that arousal acts as a reinforcer in both

an operant (e.g. intermittent wins) and in a classical (e.g. gambling stimuli, such as the “smell”

of a casino) conditioning sense (Blaszczynski & Nower, 2002). Sharpe et al. (1995) performed

an extensive analysis of arousal in both problem and non-problem gambling. They found that

gambling stimuli alone, even in the absence of any actual gambling activity, were sufficient to

evoke increases in physiologic arousal in problem gamblers (Sharpe, Tarrier, Schotte, & Spence,

1995).

Previous work in our lab (Lee & Dixon, unpublished) set out to examine a topic at the

forefront of most dedicated poker player’s minds: is it possible to tell, either overtly or with

physiologic measures, when a person is bluffing? Twenty-four healthy, non-problem gambler,

volunteers were tested in triads. Although the player’s believed the decks were completely

randomized, the game’s card decks were actually pre-arranged so that each participant would

receive two strong hands and four weak hands for a combined eighteen hands. Before play

commenced each participant was given confidential written instructions that, should they

encounter a specific weak hand, they were to bluff (to play the weak hand to win). Consequently,

within the eighteen pre-arranged decks there were, for each player: two strong hands, two weak

hands and two weak hands for which the players were to bluff. The dependent variables selected

were chosen to measure both overt and covert effects of bluffing. These included number of eye

blinks, hand gestures, mean heart rate (HR) and mean skin conductance levels (SCL).


Although no significant findings were obtained for the eye blink or hand gesture

variables, there were two significant effects for HR and SCL. During the first recording epoch,

HR was significantly higher for bluff and win hands than for fold hands. During the second

epoch, SCLs were significantly higher for bluff hands over strong hands, as well as strong hands

over fold hands. Lee and Dixon interpreted these data as an interplay between cognitive load and

arousal. They hypothesized that the initial increases in HR for strong and bluff hands, during the

first epoch, mostly reflected an increase in cognitive load. That is, participants mentally worked

to acquaint themselves with game play in a novel environment (in the lab, with electrodes

affixed) and more importantly, worked to decide how much to bet in order to maximize their

chances of winning. Lee and Dixon hypothesized that, together, these demands amounted to a

partial depletion of the participants’ cognitive resources during the first epoch. This would have

precluded the participants from actively engaging in the deception required in bluffing and thus

explained the negligible increases in arousal (as measured by SCL) during epoch one. During the

second epoch, having been acclimatized to game play, the players would have had sufficient

cognitive resources available to actively engage in deception. This would explain the elevated

SCL during the bluff hands in the second epoch.

One potential criticism of the Lee and Dixon study was the exclusive reliance on

psychophysical measures to assess arousal. Their hypotheses concerning arousal, cognitive load,

and how these factors interact with hand type would have been strengthened had subjective

measures of arousal supplemented the objective measures.

The current study has three purposes: replication of the previous study, adding subjective

measures of arousal to complement psychophysical measures of arousal, and a specific


investigation of the effects of cognitive load on poker gameplay. To more closely examine

cognitive load we will be making two additions to Lee and Dixon’s experimental design: a

subjective rating of arousal and a baseline-mental arithmetic task utilizing heart rate variability

(HRV). To compare the participant’s subjective feelings of arousal with objective measurements

of cognitive load we will use a 9-point Likert-type visual analogue scale (VAS; Morris, 1995). To

analyze their theory on the interaction of cognitive load and poker gameplay we will also have

the participants complete a baseline-mental arithmetic task. Specifically the serial sevens

subtraction task (Pennington, 1947; Smith, 1967; Frigy, Varga, Orbán, & Incze, 2005). For this

task we will utilize HRV measures as it has been suggested they are an accurate indicator of

cognitive load (Gunther Moor, Crone, & van der Molen, 2010; Verkuil, Brosschot, Borkovec, &

Thayer, 2009). HR differs from HRV in its composition. As opposed to measuring the mean rate

at which the heart beats, HRV is a number of statistical processes performed on a series of inter-

heartbeat intervals recorded over an epoch of interest (Clifford, 2002).

Overall, we intend to measure cognitive load and arousal in volunteer participants as they

play Texas Hold ‘em poker. During gameplay, we expect cognitive load to be higher during the

first epoch than during the second. We predict these maximal levels of cognitive load will

abolish any arousal responses to the differing hand types, indicated by indifferent SCL responses.

In the second epoch, after the participants become familiar with the game, HR will decrease for

bluffing and strong hands (becoming equivalent to fold hands), but SCL will now differentiate

the hand types: bluff hands will have the highest SCLs, strong hands next highest, and fold hands

the least.


Methodology

Design

Our study was a two-factor, repeated-measures design. The first factor was type of hand

(strong hand, weak hand, bluff hand). The second factor was epoch (the first iteration during

which a participant received each of the three hands and the second iteration). In addition we

recorded a baseline and a mental arithmetic task for each participant.

Procedure

After entering the testing facility and completing informed consent procedures

participants were asked to wash their hands prior to electrode attachment. Gender-matched

research assistants applied the five experimental electrodes. Three participants were seated at a

mock Texas Hold ‘em gameplay table facing a dealer (a research assistant). After a ten-minute

acclimatization period a 60s baseline epoch was recorded. Following this, participants completed

a mental arithmetic task for 60s. The task was the serial sevens subtraction task in which

participants, beginning at the number 100, silently count backwards by sevens (Smith, 1967).

Following the baseline and mental arithmetic tasks, the dealer administered specific poker

instructions (rules of the game). They were also told that when they saw specific card

combinations they were to bluff. To ensure players understood the instructions the dealer then

dealt two “face-up” rounds of poker, explaining each round and suggesting the best course of

action to each player. Participants were informed that each would receive a prize (chips, candy

bars) commensurate with the amount of playing chips they had at the conclusion of the

experiment. After verbally ensuring each player was comfortable with the rules and general


gameplay the eighteen experimental rounds of poker began. These eighteen rounds were played

in a randomized order, selected before the experiment commenced.

Participants

Research participants were 39 University of Waterloo students (24 male, Mage = 20.6

years, age range: 19-25 years), completing the experiment in return for course credit. To be

eligible for the experiment participants must have been: 1) between the age of 19 and 65 (the

former being the legal age for gambling in the province of Ontario); 2) not currently taking any

anxiety reducing medication (as this would interfere with psychophysical measures); and 3) not

currently be in treatment for problem gambling.

Measures

A visual analogue scale (VAS) was used to allow participants to record their subjective

ratings of arousal. At the conclusion of each hand, participants confidentially indicated on a 9-

point Likert scale how physiologically aroused they felt during the last round of gameplay. A

score of one corresponded to “Not at all aroused” and nine to “Very physiologically aroused”.

The VAS scale uses images of a cartoon character to depict varying levels of arousal in the case a

participant is not familiar with the concept of physiologic arousal (Morris, 1995; see Appendix

1).

Skin conductance levels (SCL) were used to measure the participants’ objective levels

of arousal during strong, weak and bluffing hands. To minimize the effect of baseline drift over

time we used delta SCLs. The participant’s skin conductance rate (measured in microsieverts;

µSv) at the beginning of the epoch of interest was subtracted from the highest maxima achieved

throughout the epoch.


Heart rate (HR) data were used to measure the participants’ levels of cognitive load.

Mean beats per minute (bpm) were calculated from an average of interbeat intervals, producing a

mean instantaneous heart rate (HRi) for each epoch of interest.

Heart rate variability (HRV) data were used to assess Lee and Dixon’s contention that

increased levels of cognitive load lead to their unexpected results (increased HR in epoch one for

strong and bluff hands, without any effect on SCL). HRV data consist of various statistical

operations performed on a series of interbeat intervals, recorded over an epoch of specific length

(Task Force of the European Society of Cardiologists, 1996). Given the ultra-short duration of

our recording epochs (60s) we limited our HRV analysis to time-domain measures. The current

consensus of the literature is that frequency-domain analysis of epochs of less than five minutes

duration is ill-advised as the data are too susceptible to artifact. We used three redundant

measures, as the current recommendations are to use two or more when performing HRV

analysis within a psychophysiological study (Clifford, 2002). We selected the three most popular

time-domain measures: SDNN, NN50 and RMSSD. As opposed to increasing in response to

increased cognitive load (as HR does), HRV decreases. This signals a reduction in the variability

of the interbeat intervals. Heartbeats deemed to be artifact in nature were not included in these

analyses.

SDNN is the standard deviation, of the intervals between each pair of adjacent normal

heartbeats (all non-artifact, non-ectopic R-R intervals).

NN50 is the number of successive, normal, interbeat intervals which differ by more than

50 ms. This range is somewhat arbitrarily selected and is used as another indicator of the

variability of the heart rate over an epoch of interest.


RMSSD is a rather complicated statistical operation: the root, of the mean, of the square,

of the standard deviations, of all of the intervals between normal heartbeats over an epoch of

interest.

Apparatus

SCL, HR and HRV information were acquired through an eight-channel AD Instruments

Powerlab (model 8/30; Powerlab; Colorado Springs, CO, USA). SCL data were collected via two

velcro-electrodes placed at the distal phalanges of the index and ring fingers of the participant’s

non-dominant hand. HR and HRV data were collected from three self-adhesive electrodes placed

on the participant’s skin in a modified Mason-Likar arrangement (Mason & Likar, 1966; see

Appendix 2). This arrangement places two electrodes in the infraclavicular fossae, 2cm medial to

the deltoid border. A third electrode, acting as an earth ground, is placed on the abdomen in the

left anterior axillary line, 3-4 cm superior to the iliac crest. The Mason-Likar arrangement

considerably reduces movement artifact from skeletal muscle allowing the participants to move

more freely during gameplay, an otherwise considerable challenge to the ecological validity of

the study.

Data files were analyzed in LabChart Version 7.2.3 on a MacBook laptop. Exclusions

were set with a macro to analyze only the twenty 60s epochs of interest (baseline, mental

arithmetic and eighteen poker hands) for each participant. As HRV analysis cannot be conducted

on epochs of unequal lengths (Clifford, 2002) any quickly played poker hands resulting in an

epoch of less than 60s were excluded. A digital bandpass filter was applied to the data signal with

frequencies individually selected to optimize the signal-to-noise ratio of R-waves to other cardiac

waveforms. All variables were outlier-corrected to within 2.5 standard deviations of their mean.


Results

Data Attrition

Unfortunately, given the complexity of the experiment, a considerable portion of

experimental epochs were lost. As players engaged in poker gameplay (reaching across the table

to pick up cards or place their bets) movement artifact rendered some epochs completely

unanalyzable. Compounding these losses is the aforementioned criteria that all HRV epochs to be

compared must be of equal duration. Taken together these factors caused the sample size to be as

low as 20 cases in some of the dependent variables (detailed specifically in the ensuing tables).

Arousal and Cognitive Load

As summarized in Table 1.

Table 1Arousal and Cognitive Load

VAS (n=33)VAS (n=33) Δ SCL (n=24)Δ SCL (n=24) HR (n=34)HR (n=34)

Type of Hand Mean SD Mean SD Mean SD

First EpochFirst EpochFirst EpochFirst Epoch

Strong 6.58 1.275 2.72 2.226 82.75 10.835

Weak 3.36 1.558 1.483 1.532 80.52 11.274

Bluff 5.82 1.811 2.603 1.187 85.60 10.789

Second EpochSecond EpochSecond EpochSecond Epoch

Strong 5.73 1.275 2.41 1.768 83.81 10.821

Weak 3.45 2.181 1.548 1.152 80.31 11.478

Bluff 5.00 2.107 1.960 1.587 81.55 9.011

Note: VAS was based on a 9-point Likert scale, Δ SCL measured in µSv, HR measured in mean bpm.

VAS had a main effect of type of hand, F(2,64) = 39.761, MSE = 3.340, p<.000, n2 =

0.554. In addition, there was a main effect of epoch order, F(1, 32) = 6.013, MSE = 2.271, p<.02,


n2 = 0.158. The type of hand by epoch order interaction was not significant, F(2, 64) = 1.866,

MSE = 2.52, p<n.s.. To further understand the type of hand main effect we conducted

Bonferroni-corrected pairwise comparisons. Participant’s subjective ratings of their arousal

during strong hands (M=6.342) were significantly higher than during both bluff hands

(M=5.434), t(75) = 3.543, SEM = 0.256, p<.001, and weak hands (M=3.533), t(76) = 10.694,

SEM = .266, p<.000. As well, bluff hands were significantly higher than weak hands, t(75) =

6.182, SEM = .302, p<.000.

SCL had a main effect of type of hand, F(2,46) = 6.775, MSE = 2.078, p<.003, n2 =

0.228. Neither the epoch order, F(1, 23) = 1.175, MSE = 2.692, p<.n.s., nor the type of hand by

epoch order interaction, F(2, 46) = 0.841, MSE = 1.797, p<n.s., were significant. Bonferroni-

corrected pairwise comparisons conducted on type of hand revealed participant’s objective

arousal levels during strong hands (M=2.562) were significantly higher than during weak hands

(M=1.515) t(47) = 20.907, SEM = .343, p<.017. Bluff hands (M=2.281) were significantly higher

than weak hands as well, t(47) = 30.008, SEM = .175, p<.001. Bluff and strong hands were not

significantly different, t(47) = 5.747, SEM = 0.334, p<n.s..

HR had a main effect of type of hand F(2,66) = 8.175, MSE = 25.327, p<.001, n2 =

0.199. In addition, there was a main effect of the type of hand by epoch order interaction, F(2,

18) = 6.091, MSE = 19.725, p<.004, n2 = 0.156. Epoch order was not significant, F(1, 33) =

1.951, MSE = 29.592, p<.n.s. Bonferroni-corrected pairwise comparisons of the main effect of

type of hand indicated that participant’s heart rates during strong hands (M=83.278) were

significantly higher than during weak hands (M=80.413), t(67) = 24.764, SEM = 0.947, p<.014.

Bluff hands (M=83.572) were significantly higher than weak hands as well, t(67) = 32.403, SEM


= 0.798, p<.001. Strong and bluff hands were not significantly different, t(67) = 2.872, SEM =

0.838, p<.n.s.. To unpack the type of hand by epoch order interaction, we conducted simple

main effects comparisons to analyze the two epochs separately. Bonferroni-corrected pairwise

comparisons indicated that, in epoch one, bluff hands (M=85.594) lead to significantly higher

heart rates than strong hands (M=82.741), t(33) = 2.61, SEM = 1.092, p<.014, and than weak

hands (M=80.518), t(33) = 3.99, SEM = 1.271, p<.000. In epoch two, strong hands (M=83.809)

lead to higher heart rates than weak hands (M=80.309), t(33) = 2.7, SEM = 1.294, p<.011. Bluff

hands (M=81.549) were not significantly higher than weak hands in the second epoch t(33) =

1.27, SEM = 0.977, p<n.s..

Baseline-Mental Arithmetic task

As shown in Table 2, skin conductance levels were not significantly different between the

resting baseline condition and the mental arithmetic task. As previously noted, HRV has been

used to track cognitive load. Of the three HRV variables only SDNN significantly responded to

the increases in cognitive load brought on by the mental arithmetic task, t(37) = 2.948, SEM =

2.403, p<.006. As such this measure was used to address whether there was a preferential

increase in cognitive load during the first epoch of the poker task.

Table 2Four variable analysis of the Baseline-Mental Arithmetic task

Δ SCLΔ SCL SDNNSDNN NN50NN50 RMSSDRMSSD

Mean SD Mean SD Mean SD Mean SD

Baseline 0.759 1.167 52.986 16.035 13.3 9.17 47.476 24.593

Mental Arithmetic 1.055 1.853 45.902 14.077 11.5 9.61 42.561 26.511

Difference 0.296 -7.084*-7.084* -1.8 -4.915

Note: Δ SCL measured in µSv, SDNN and RMSSD measured in ms, NN50 is simple occurrence count; SCL N=39, SDNN, NN50 and RMSSD N=38; * p<.006; SCL, NN50 & RMSSD p< n.s.


As shown in Table 3, SDNN showed neither a main effect of type of hand, F(2, 38) =

0.218, MSE = 100.549, p<.n.s., epoch order, F(1, 19) = 1.414, MSE = 54.719, p<.n.s., or a type

of hand by epoch order interaction, F(2, 38) = 0.399, MSE = 53.217, p<.n.s.

Table 3Arousal and Cognitive Load with HRV

VAS (n=33)VAS (n=33) Δ SCL (n=24)Δ SCL (n=24) SDNN (n=20)SDNN (n=20)

Type of Hand Mean SD Mean SD Mean SD

First EpochFirst EpochFirst EpochFirst Epoch

Strong 6.58 1.275 2.72 2.226 51.034 12.840

Weak 3.36 1.558 1.483 1.532 50.819 13.841

Bluff 5.82 1.811 2.603 1.187 50.320 11.903

Second EpochSecond EpochSecond EpochSecond Epoch

Strong 5.73 1.275 2.41 1.768 48.040 11.872

Weak 3.45 2.181 1.548 1.152 50.731 12.547

Bluff 5.00 2.107 1.960 1.587 48.583 9.695

Note: VAS was based on a 9-point Likert scale, Δ SCL measured in µSv, SDNN measured in ms.


Discussion

The current study had three purposes: replication of the previous study, adding subjective

measures of arousal to complement psychophysical measures of arousal and a specific

investigation of the effects of cognitive load on poker gameplay.

Replication

For SCL measures, Lee and Dixon found that bluffing led to higher SCL levels than

either strong or weak hands but this effect only occurred during epoch two. In our study, during

epoch one, strong and bluff hands led to significantly higher SCL levels than during weak hands.

For HR measures, Lee and Dixon found significantly higher heart rates for bluffing and strong

hands in epoch one but not in epoch two. In our study, bluffing led to higher HR, than either

strong or weak hands, during epoch one. Yet during epoch two, only strong hands lead to higher

heart rates than weak hands.

Subjective measures of arousal

Our study added a subjective measure of arousal using VAS. We found that participants’

subjective ratings of arousal were highest for strong hands when compared with both bluff and

weak hands. As well their subjective ratings of arousal were higher for bluff hands than for weak

hands.

Measuring cognitive load using HRV

When baseline resting HRV was contrasted with an epoch during which participants

engaged in a task known to increase cognitive load, all three measures of HRV showed nominal

reductions in the predicted direction. However, only SDNN showed a significant response to

cognitive load. As such, with these participants, SDNN was able to strongly track increases in


cognitive load. After determining that SDNN was best suited for this analysis we examined the

type of hand and epoch order factors alongside VAS, delta SCL and mean HR. Although SDNN

reacted sharply to the mental arithmetic task there were no significant differences between

strong, weak or bluff hands. If Lee and Dixon were correct in their contention that cognitive load

increases elevated HR during bluffing and strong hands (and somehow masked SCL effects),

then we should have shown significant reductions in SDNN during bluffing and strong hands in

epoch one. However, no such effects were noted.

These data suggest that the large increases in HR during the bluff hands were not due to

increased cognitive load. By contrast, it appears that the deception and/or risk involved in

bluffing served to elevate HR. When a player bluffs, they are holding a weak hand yet proceed as

if they are playing from a position of strength. To win, the player must successfully masquerade

as if they were holding a strong hand whilst under the direct scrutiny of the other players. In

addition, there is considerable risk involved in bluffing, as players continue to bet with a hand

that may be substantially inferior to their opponents’. Outside of the gambling literature there

exists strong support for the links between deception and arousal (DeTurck & Miller, 1985;

Gödert, Rill, & Vossel, 2001) as well as between risk and arousal (Critchley, Mathias, & Dolan,

2001).

Unlike the study of Lee and Dixon, we found converging effects for HR and SCL

measures. Most importantly for both HR and SCL, significantly larger effects were noted when

players attempted to bluff with weak hands compared to when they were simply going to fold a

weak hand. We propose that this elevated state of arousal for bluffing may be one of the key

features which makes this form of poker particularly arousing.


In our study the subjective ratings of arousal were highest for strong hands, followed by

bluffing hands, and weakest for fold hands. The literature on arousal and risk led us to predict

that bluffing would lead to more subjective arousal than strong hands because of the increased

risk. In our experiment, players were forced to bluff on specific hands. By removing the choice

of when and when not to bluff, we may have reduced arousal in this condition. Importantly for

both subjective and psychophysiological measures of arousal, bluffing on a weak hand was

significantly more arousing than folding on a weak hand.

As afore mentioned, little is currently understood about the mechanisms underlying the

allure and addictive potentially of Texas Hold ‘em poker. There is much evidence to suggest that

the mechanisms involved in the attraction and addiction of other gambling mediums is arousal.

Blaszczynski and Nower (2002) cite arousal as the reinforcement process behind problem

gambling (Blaszczynski & Nower, 2002). Our data suggest that arousal features prominently

throughout poker. Beyond the excitement one would intuitively associate with playing a strong

hand to win there is the thrill involved with bluffing. These are weak hands which, without the

concept of bluffing, a player would simply toss aside with minimal arousal. Bluffing provides a

mechanism whereby even weak hands can be highly arousing. When one bluffs and wins, such

arousal could be highly reinforcing.

A recurring challenge within gambling research is ecological validity. Both the previous

study (Lee and Dixon) as well as the current study went to great pains to preserve what

authenticity is available within an experiment conducted on undergraduates within the confines

of a psychology research lab. We purchased a Texas Hold ‘em mat screened with similar

markings to those found in casinos and on televised poker tournaments. We utilized actual


gameplay chips and rewarded the participants, beyond their course credit, commensurate with

their performance. Despite these measures we would be interested to study arousal and cognitive

load in Texas Hold ‘em under even more realistic conditions. Although our participants were

unaware that the decks were pre-arranged, or that there were multiple decks at all (the dealer

concealed the swap of pre-sorted decks while shuffling), this is still far from the completely

randomized nature of normal poker gameplay. Complete randomization might be possible if one

could devise an apparatus to have each participant confidentially indicate whether their

intentions with the current hand were to play to win from a position of strength, to fold a weak

hand or to play a weak hand to win (to bluff). As well, although we endeavored to ensure at least

a baseline competency in gameplay before the experimental rounds began, it would be

interesting to quantify the participant’s skill in poker and analyze statistically whether this had a

significant effect on physiologic spectra.

If much of the revenue and addiction of the gambling world are predicated on generating

physiologic arousal then Texas Hold ‘em poker is a prime candidate. The proportion of time a

player’s arousal levels are elevated during gameplay is exaggerated thanks to bluffing. As well,

given the simple nature and design of the game, there is relatively little “downtime” in Texas

Hold ‘em. This is especially the case in on-line versions of the game, where a player can play at

multiple tables at once. This means the mind and body of a poker player is subjected to constant

stimuli and cues which increase arousal over the course of their play. As our understanding of the

game continues to improve it is entirely possible that policy change will be required to protect

future generations of poker players developing gambling problems to this potentially highly

addictive game.


References

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Appendices

Appendix 1: VAS

Adapted from: Morris, J. (1995). Observations: SAM: The self-assessment manikin.

Appendix 2: Mason-Likar electrode placement

Adapted from: Malmivuo, J., & Plonsey, R. (1995). Bioelectromagnetism


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