"To bluff like a man or fold like a girl?" - Gender biased deceptive behavior in online poker

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RESEARCH ARTICLE
"To Bluff like a Man or Fold like a Girl?" –
Gender Biased Deceptive Behavior in Online
Poker
Jussi Palomäki
1
*, Jeff Yan
2
, David Modic
3
, Michael Laakasuo
1
1Cognitive Science, Institute of Behavioural Sciences, University of Helsinki, Helsinki, Finland, 2School of
Computing & Communications, Lancaster University, City of Lancaster, United Kingdom, 3Computer
Laboratory, University of Cambridge, Cambridge, United Kingdom
*jussi.palomaki@helsinki.fi
Abstract
Evolutionary psychology suggests that men are more likely than women to deceive to bolster
their status and influence. Also gender perception influences deceptive behavior, which is
linked to pervasive gender stereotypes: women are typically viewed as weaker and more
gullible than men. We assessed bluffing in an online experiment (N = 502), where partici-
pants made decisions to bluff or not in simulated poker tasks against opponents represented
by avatars. Participants bluffed on average 6% more frequently at poker tables with female-
only avatars than at tables with male-only or gender mixed avatars—a highly significant
effect in games involving repeated decisions. Nonetheless, participants did not believe the
avatar genders affected their decisions. Males bluffed 13% more frequently than females.
Unlike most economic games employed exclusively in research contexts, online poker is
played for money by tens of millions of people worldwide. Thus, gender effects in bluffing
have significant monetary consequences for poker players.
Introduction
Deception and dishonest signaling—implicit or explicit acts to propagate false information—
can be observed throughout the animal kingdom [1]. In humans, deception is a part of the “flip
side”of cooperation in communal living [2]: through evolution, we learned to cooperate, but
also to manipulate others in order to gain utility and avoid conflict [3]. Deceiving others is now
an integral part of our behavior, from innocuous every-day (i.e. white) lies [4] to more elabo-
rate cons, for example lonely hearts swindles [5].
Deceptiveness has been linked to intra- and intersexual competition, risk preferences and
gender differences [6]. Ancestral human male reproductive fitness was constrained by access to
females, and female fitness by factors related to providing for offspring survival [7]. Access to
females and reproduction is a resource for which males engage in risky competition against
one another [8], and in costly signaling directed towards females [9]. In modern men, this
competition is reflected in deception to obtain status and influence, which indirectly result in
PLOS ONE | DOI:10.1371/journal.pone.0157838 July 6, 2016 1/13
a11111
OPEN ACCESS
Citation: Palomäki J, Yan J, Modic D, Laakasuo M
(2016) "To Bluff like a Man or Fold like a Girl?" –
Gender Biased Deceptive Behavior in Online Poker.
PLoS ONE 11(7): e0157838. doi:10.1371/journal.
pone.0157838
Editor: Marina A. Pavlova, University of Tuebingen
Medical School, GERMANY
Received: February 17, 2016
Accepted: June 6, 2016
Published: July 6, 2016
Copyright: © 2016 Palomäki et al. This is an open
access article distributed under the terms of the
Creative Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original author and source are
credited.
Data Availability Statement: The authors have
made their data publicly available at: http://dx.doi.org/
10.6084/m9.figshare.1172112. Their preliminary (a
priori) plan for the data analyses is available at: http://
dx.doi.org/10.6084/m9.figshare.1015623.
Funding: This study was part of an EPSRC
(Engineering and Physical Sciences Research
Council)–funded research project “The Deterrence of
Deception in Socio-Technical Systems”(EP/
K033476/1) (http://gow.epsrc.ac.uk/NGBOViewGrant.
aspx?GrantRef=EP/K033476/1); CO-Principal
Investigator: JY. The funders had no role in study

better access to women [10]. Specifically, males are more likely to lie to secure a monetary ben-
efit [11,12], whereas females are generally seen as more cooperative and less exploitative [13];
although not all researchers agree with this [14]. The male competitive behavior also differs
depending on the gender of their opponent [15]. In particular, male deception in intersexual
competition (towards females) seems to be linked to status signaling [16,17].
Gender stereotyping is present across contexts. For example, female trivia game contestants
are often expected to be weaker competitors than their male counterparts [18], and car dealers
will frequently quote higher prices to female as opposed to male customers [19]. In addition,
females are perceived to be more easily misled and deceived when it comes to negotiations
[20].
These findings extend to game theory, where Holm [21] demonstrated that irrespective of
gender of the initiator, females would receive low-ball offers in a resource allocation task (i.e.
they would be “hawkishly preyed upon”). Females would also be more likely recipients of
attempts to deceive in a bluffing game [22]. Similarly, in the ultimatum game, both females and
males were likely to choose a higher minimum acceptable offer when facing a female opponent
[23,24]; offers made to men were higher than offers made to women [23,25], and women were
more likely than men to accept lower offers [25]. Evidence thus suggests that both men and
women display gender-dependent behavior in the context of competitive economic games
involving incentives for selfishness and deception.
In contrast, in two-player trust games—where one player first sends to the other some
amount of initial resources, which are then multiplied by a factor and distributed at the receiv-
er’s discretion among the two players—participants have been found to send more resources to
opposite gender partners than to same gender ones [26]. Males were also more cooperative
towards females than males, especially when the females were smiling [27]. It is possible females
receive more cooperative offers in trust games because they seem more trustworthy, but are
defected against or “betrayed”in games involving deception and/or competition because they
seem “weaker”or less dominant than males. Evidence suggests that characteristics such as
“warm”,“trustworthy”,“friendly”, and “weak”are more often associated with females than
males [28]. Moreover, similar patterns of results (i.e., females being offered more resources than
males) have not been observed in studies using the dictator game, which is comparable to trust
games but lacks the elements of trust and reciprocation: the initial resources are distributed
between the two players at the sender’s(the “dictator’s”) discretion, and the receiver has no
active role (see [29] for a review).
However, economic games are designed for laboratory use—results from studies employing
them may not be generalizable or predictive of behavior in more naturalistic environments.
Moreover, economic laboratory experiments typically have a very self-selected sample of par-
ticipants (e.g., high-achieving students who have a tendency to want to please the experiment-
ers; [30]). In economic games, such participant characteristics have a significant impact on
behavior in terms of altruism, fairness and resource allocation. Thus, it is difficult to estimate
the real life economic consequences of the decisions made in these games.
Unlike traditional economic games, poker is played frequently by more than a hundred mil-
lion people worldwide—a card game where deception is the norm: no apparent ethical or social
pressure prevents players from deceiving in poker. Game-theoretically, deception is also neces-
sary to increase chances of winning [31]. The most common form of deception in poker is
bluffing, which refers to betting or raising (showing strength) with a weak holding to make the
opponent fold (give up). In other words, bluffing is misrepresenting the strength of one’s poker
hand to trick opponents into believing the hand is (much) stronger than it actually is.
Poker is also a competitive, male-dominated game surrounded by strong gender stereotypes
[32], and thus offers an excellent platform to study the social aspects of strategic deception.
Gender Biased Deceptive Behavior in Online Poker
PLOS ONE | DOI:10.1371/journal.pone.0157838 July 6, 2016 2/13
design, data collection and analysis, decision to
publish, or preparation of the manuscript.
Competing Interests: The authors have declared
that no competing interests exist.

Male poker players often view female opponents as novices who are unwilling to take risks and
easily intimidated [33]. Many also feel that females do not belong at the poker table, which is a
place for “gutsy bluffs and betting warfare”[34]. Arguably, the stereotypical good (“strong”)
poker player is aggressive, masculine, and fearless—hence the colloquial saying: “Don’t fold
like a girl!”.
Poker is most frequently played online, where gender can be conveyed via avatars—graphi-
cal representations of players at a virtual table. The use of avatars in online poker is pervasive,
and typically the avatar genders are clearly depicted [35]. Given how common bluffing is in
poker, even small gender effects in players’bluffing propensity might have highly significant
monetary consequences especially for online poker players. Bluffing decisions quickly accumu-
late and become a substantial source of either wins or losses. Marginal changes in bluffing fre-
quency might translate to thousands of dollars won or lost within weeks, or even days: Many
active online poker players have been dealt millions of poker hands, and some are even able to
play on 24 virtual tables simultaneously [36].
We hypothesized that players bluff more frequently at online poker tables with primarily
female avatar opponents than at tables with primarily male avatar opponents. More impor-
tantly, we aimed to demonstrate that online poker is a useful tool in psychology, potentially
offering new insights to the literature on deception, social psychology, and poker itself.
Method
Participants
An online survey created with Qualtrics (www.qualtrics.com) in English was advertised on var-
ious international online poker web-forums. Five hundred and fifty-eight participants com-
pleted the survey. Small effect sizes have been reported in previous online poker studies, calling
for large sample sizes to increase statistical power [37]. Our data collection stopping rule was
180 participants per condition. However, based on a priori data exclusion criteria, we expected
a final sample size of 160–170 participants per condition. Fifty-six participants were omitted
due to insufficient skills in written English (skills not reported as “very good or better”). The
final sample size was 502 (N = 502; 435 males, 36 females, 31 unreported; M
age
= 29.99;
SD
age
= 8.72; age ranged from 16 to 67). These demographics are consistent with previous stud-
ies sampling poker playing populations [37]. Participants were offered the possibility of taking
part in a draw of five separate $50 Amazon.com gift coupons. This study was approved by the
Newcastle University ethics committee.
Procedure
Upon opening the questionnaire, participants gave informed consent and were randomly
assigned to one of three conditions in a between-subjects design. Participants first completed
the covariate measure of poker experience and three other measures unrelated to current
aims, followed by the dependent measure/variable (bluffing tasks), manipulation checks, and
demographics.
Participants made bet / do not bet (i.e., bluff / do not bluff) decisions in four simulated
poker tasks, in which they were “sitting”at an online poker table with four opponents repre-
sented by avatars and the names “Opponent 1–4”. The between-subjects factor “Avatar Bal-
ance”had three levels referring to the total number of female and/or male opponents (avatars)
at the table: 1) all female, 2) all male, and 3) two male and two female (“mixed”) avatars (cf. S1
Supporting Information p. 1 for pictures). The tasks were otherwise identical across these three
conditions and presented in random order.
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Materials
Avatar Creation and Pretest. Facegen Modeller v3.5 (Singular Inversions, www.facegen.
com) was used to create male and female faces of similar age (around 30) with neutral expres-
sions and various hairstyles. Facegen Modeller is frequently used to create realistic human
faces in psychological research [38]. In total, 18 male and 18 female faces were created for pre-
testing. An online questionnaire was distributed via Amazon Mechanical Turk, asking 100 par-
ticipants to evaluate the faces in a random order, one per page, on Likert 1 (“not at all”)–7
(“very”) scales on the following attributes: friendliness, trustworthiness, warmth, competence,
likability, dominance, threat, and attractiveness. To obtain avatars differing mainly with respect
to their perceived gender, we selected four female and four male faces that were the “most neu-
tral”with respect to all evaluated attributes (mean values ~4).
Covariate (Poker Experience Scale [PES]). PES has been shown to predict mathematical
accuracy in poker decision-making and used in several studies to measure players' poker skill
and knowledge [37,39]. Because bluffing also involves skill, it is likely that poker experience
influences bluffing behavior. Thus, we included PES as a covariate. The scale consisted of three
11-point and one 10-point Likert items, which measure 1) amount of years played, 2) average
level of stakes played, 3) amount of poker hands played, and 4) level of self-perceived “profes-
sionalism”in poker (cf. S1 Supporting Information p. 4 for item details). The 4-item scale
(M= 5.23, SD = 2.12, range = 1–10) was normally distributed and had a satisfactory inter-item
reliability (Cronbach's alpha = .80). Higher scores indicate higher level of poker experience and
skill.
Dependent Variable (Bluffing Measure). Participants undertook four simulated online
poker tasks involving the most popular poker variant, No Limit Texas Hold’em (NLHE). The
task was in a cash-game format, i.e., not a tournament format: the chips at play represented cash
(hypothetical US dollars), and not tournament points. Participants first read detailed task
instructions and indicated having understood them (by self-report; cf. S1 Supporting Information
p. 2). It is worth noting that studying NLHE experimentally is very difficult due to its complexity,
but with care, this complexity can be reduced by evaluating specific aspects of the game without
losing ecological validity. Below, we first briefly introduce the rules of NLHE, and then explain
how our bluffing tasks were constructed.
In NHLE, two cards are first dealt face down to each player, followed by a round of betting
(period called pre-flop). Then, five community cards that can be used by all players are placed
face-up on the table. The first three community cards are called the flop, and the last two cards
the turn and river, and there is a round of betting after each. This period of play beginning with
pre-flop and ending at latest on the river is called a hand.
During a NLHE hand, players have multiple opportunities to bluff during pre-flop, on the
flop, turn, and river. Bluffs on the river almost always end the hand (they are either called
down or folded against; a bluff on the river might get raised, but in these cases the bluffer
almost always gives up and folds), whereas other bluffs are sometimes followed by another
round of betting (if a bluff on the turn is called, another round of betting takes place on the
river). For simplicity, participants in our experiment bluffed only on the river.
In order to make an informed bluffing decision on the river, it is highly important for play-
ers to know what the opponents’preceding betting actions were. A realistic bluffing task needs
to give participants this information. We presented each task as an animated sequence of
“automated”betting actions beginning pre-flop, and ending on the river, upon which partici-
pants decided to either bet or check (not bet). Participants could not influence these automated
actions. We consulted professional poker players to make sure the game scenarios were as real-
istic as possible for NLHE with five players, and emulated as closely as possible the graphical
Gender Biased Deceptive Behavior in Online Poker
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outlook of the tables used by the most popular online poker site, www.pokerstars.net. See
http://www.comp.lancs.ac.uk/~yanj2/poker/ for the tasks exactly as they were presented to par-
ticipants (the tasks are presented as animated frame-by-frame sequences of betting actions,
and viewing each task takes about 80 seconds).
The opponents (avatars) and the participant were “sitting”in the same table position across
the four tasks. Participants made one decision against each opponent, and each decision was
made in a heads-up situation (one versus one; the other three opponents had folded their cards
and were not “in the hand”).
If the participant decided to bet, s/he first indicated the amount to bet (hypothetical US
dollars) and then whether or not the bet was a bluff (“Was your bet a bluff? Yes/No/I don’t
know”). Only the bets that participants reported being bluffs were analyzed as actual bluffing
decisions. Of all the betting decisions made by all the participants, 97% were reported to be
bluffs. This indicates we were successful in creating the tasks in a way that the only sensible rea-
son to bet would be to bluff. The DV “Average Bluffing Frequency”was calculated for each par-
ticipant as the total number of bluffing decisions (maximum = 4, minimum = 0) divided by
four (M= 0.45, SD = 0.23).
Results
Manipulation Checks
After the poker tasks, participants answered the following question on a Likert scale 1
(“completely disagree”)–7(“completely agree”): “The gender of the opponents' avatars influ-
enced my decisions”. Participants were then presented with pictures of the poker task avatars,
in a randomized order and one per page, and asked to evaluate them on the same attributes
that were used in the pretest (see S1 Supporting Information p. 11). Participants were also
asked to indicate the gender of the avatar picture (“Is this person: Male/Female”).
The majority of participants disagreed with the statement “The gender of the opponents’
avatars influenced my decisions”(M= 1.39, SD = 0.97). The average avatar gender recognition
rates were 93.5%, 99.7%, and 98.7% in the “all female”,“all male”, and “mixed”avatar groups,
respectively.
Main Analyses
A one-way ANCOVA was run with Average Bluffing Frequency as the DV, Avatar Balance as
a between-subjects factor, and Poker Experience Scale as a covariate. There was a significant
main effect of Avatar Gender on Average Bluffing Frequency (F(2, 498) = 3.83, p= .022, par. η²
= 0.015). Planned contrast analysis revealed that participants bluffed more frequently at tables
with all female avatars than with all male avatars (B = 0.061, F(1, 498) = 6.41, 95% CI [0.01,
0.11], p= .012). Participants also bluffed more frequently at tables with all female avatars than
at gender mixed tables (B = 0.055, F(1, 498) = 5.10, 95% CI [0.007, 0.10], p= .024). Participants
with more poker experience bluffed more frequently (B = 0.039, F(1, 498) = 69.3, 95% CI [0.03,
0.048], p<.001, par. η² = 0.122). See Fig 1 and Table 1 for model statistics.
Supplementary Analyses
Supplementary analyses were performed by entering gender, age, education and income as covar-
iates (cf. S1 Supporting Information pp. 9–10 for statistics on education and income) and omit-
ting data from the participants who did not recognize the avatar opponent genders with
100% accuracy or had missing demographic data. Males bluffed more frequently than females
(B = 0.129, F(1, 427) = 10.1, 95% CI [0.05, 0.21], p= .002). The younger the participants were, the
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more frequently they bluffed (B = -0.003, F(1, 427) = 6.515, 95% CI [-0.006, -0.001], p=.011).
The main findings were robust to adding the demographic controls into the model. The
current study included also measures (questionnaire scales) unrelated to the current aims and
hypotheses. The main results were robust to including these measures into the models as covari-
ates. The results concerning these measures have been reported in [40]. Consult S1 Supporting
Information pp. 5–6 for more details, including a data transparency table.
As a post hoc analysis, we also contrasted the”all female”group with the combined mean of
the”all male”and”gender mixed”groups (”all female”vs.”gender mixed”+”all male”). This
contrast was statistically highly significant (B = 0.12, F(1, 498) = 7.59, 95% CI [0.03, 0.20],
p= .006). Given the gender imbalance in our sample, we re-ran all our analyses by excluding all
female participants. This did not significantly affect the results.
Expected Monetary Value of Bluffing
In order to evaluate the monetary implications of the observed 6.1% difference in bluffing fre-
quency (cf. Fig 1), we performed post hoc expected value calculations. These were made
possible by having data on the participants’bluffing frequency and their average bluff sizes.
Specifically, we calculated the expected value of calling the bluffs made by the participants—or,
in other words, the monetary implications for the hypothetical opponents in our experiment.
Below, for clarity, we will refer to the hypothetical opponent as “Player 1”and the participant
as “Player 2”.
Fig 1. Average bluffing frequency (%) in the four poker tasks. Results are presented separately for three
experimental conditions: poker table with 1) gender mixed (two female and two male), 2) all male, and 3) all
female avatar opponents. The model is adjusted for Poker Experience Scale. Planned contrast between “all
male avatars”and “all female avatars”groups is significant: B = 0.061, F(1, 498) = 6.41, 95% CI [0.01, 0.11],
p= .012. Error bars represent 95% confidence intervals.
doi:10.1371/journal.pone.0157838.g001
Table 1. ANCOVA statistics. Average Bluffing Frequency is the dependent variable.
Factor df F p par. η²
Avatar Balance 2 3.83 .022 015
Poker Experience Scale 1 69.3 <.001 .122
Model statistics: F(3, 498) = 24.5, p<.001, adj. R
2
= 0.123). The factor “Avatar Balance”has three levels
referring to the number of female and/or male opponents (avatars) at the virtual online poker table: 1)
gender mixed (two female and two male), 2) all male, and 3) all female avatars.
doi:10.1371/journal.pone.0157838.t001
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Across the four tasks, the average size of the pot (amount of contended money) on the river
was $301.7, and the average bluff sizes were $212 (SD = 91.9) and $210 (SD = 93.6) at the all
male and all female tables, respectively. Player 1 needs to call these amounts for a potential
gain (equaling the size of the pot before calling) of $212 + $301.7 = $513.7 (all male table) or
$210 + $301.7 = $511.7 (all female table). For simplicity, we make the following assumptions:
1. Generally, a bet on river (see Glossary in S1 Supporting Information p. 3) in online poker is
either made as a bluff or “for value”(i.e. in hopes of getting called by a worse hand)
2. In actual online poker, in similar “river positions”as those used in our experiment, the
player who is betting is typically equally likely to have a strong hand as a weak one, and very
unlikely to have a hand of medium strength. Therefore, we assume Player 2 will have a
strong hand (i.e. a winning hand) 50% of the time, and a weak hand (i.e. a losing hand) 50%
of the time. Although this assumption is a simplification, it is based on the feedback from
two professional poker players we have consulted. The exact distribution of hand strengths
in similar river positions is impossible to ascertain, given the imperfect information nature
of the game
3. In similar river positions as those used in our experiment, strong hands will always bet “for
value”(i.e. they will never “check”) and the bet sizes will be similar to those of the bluffs
Given these assumptions, we can extrapolate the results from the current experiment to
actual online poker. Player 2 will have a strong/winning hand 50% of the time, of which s/he
will bet for value P
valuebet
= 1 = 100% of the time; and a weak/losing hand 50% of the time, of
which s/he will bet (i.e. bluff) P
bluff
of the time. We obtain the values of P
bluff
from our experi-
mental observations: The average bluffing frequencies were 43.1% (at all male tables) and
49.2% (at all female tables; cf. Fig 1). Note that every bet made with a weak hand is by definition
a bluff, because all bets in our experiment were made with weak hands, and only bets that were
considered to be bluffs were included in the analyses. Thus, following the Bayes’theorem nota-
tion, the probability of Player 2 having a weak/losing hand given s/he’s betting (i.e. the poste-
rior probability of Player 2 bluffing; see Fig 2 for a decision tree illustration), is:
PðAjBÞ¼ Pð“Player 2 has a weak hand”j“Player 2 bets”Þ
¼PðA\BÞ
PðBÞ¼PðBjAÞPðAÞ
PðBÞ
¼Pð“Player 2 bets”j“Player 2 has a weak hand”ÞPð“Player 2 has a weak hand”Þ
Pð“Player 2 bets”Þ
¼Pbluff 0:5
Pvaluebet 0:5þPbluff 0:5¼Pbluff
Pvaluebet þPbluff
At all male tables this yields:
PðAjBÞ¼ 0:431
1þ0:431 0:3012
At all female tables:
PðAjBÞ¼ 0:492
1þ0:492 0:33
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Because P(A|B) is the posterior probability of Player 2 bluffing, it is equal to the probability
of Player 1 winning by calling. Thus, the expected value of calling is:
PðAjBÞ½Size of the pot before callingð1PðAjBÞÞ  ½Size of the bet
At all male tables, this yields:
0:3012 $513:70:6988 $212 $6:53
At all female tables:
0:33 $511:70:67 $210 $28:16
Thus, we estimate that calling the bets made by Player 2 would be $28.16 −$6.53 = $21.63
more profitable per bet when done at all female tables, compared with all male tables.
Discussion
We evaluated gender effects in online poker bluffing. Participants were more likely to bluff
with opponents represented by female avatars than with male or gender-mixed avatars. Supple-
mentary analyses revealed that male participants also generally bluffed more frequently than
females.
These results are consistent with previous studies evaluating two distinct aspects of gender
effects in competition and deception in economic games. Firstly, Holm [21,22] and Solnick
Fig 2. Decision tree for the actions of player 2. The probabilities for “Strong hand”and “Weak hand”are
derived from Assumption 2, and the probability for P
valuebet
is derived from Assumption 3. The probabilities
for P
bluff
are obtained from our experimental data. “All ♂table”and “All ♀table”refer to the experimental
conditions “all male table”and “all female table”, respectively.
doi:10.1371/journal.pone.0157838.g002
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[23] found that knowledge of co-player gender influenced both male and female participants'
behavior: both behaved aggressively when they believed their co-player was female. Secondly,
our results tentatively support Dreber and Johannesson [11] and Erat and Gneezy [12], who
found that males were more likely than females to deceive for a monetary benefit.
Poker highlights gender stereotypes and intersexual competition. Often the callous purpose
of the game is to win as much money (and fame) as possible at other players' expense. Consis-
tent with Holm [21], participants' bluffing behavior in the current experiment can be viewed as
“hawkish”; female avatars might be regarded as opponents who are unlikely to “fight back”,
and easy targets for bluffing. Thus, female players presumably get “preyed upon”in competi-
tive games involving deception due to stereotypes in opposition with a good, “strong”(male)
player. This interpretation is also consistent with Kray et al. [20], who found that women are
deceived more often than men in negotiations due to their lower perceived competence. Taken
together, these results help explain why similar gender-conditioned behavior has not been
prominently observed in the context of trust games, where success depends on the “benevo-
lence”(and not the “gullibility”) of one's co-player [26,27].
We expected that bluffing frequency would increase in the order of “all male”<“gender
mixed”<“all female”tables (groups). However, there was no significant difference between
the “all male”and “gender mixed”groups. If a player decides to bluff a female avatar at a gen-
der mixed table, the decision is made in the presence of male avatars. Sensible players should be
cautious not only of their current “heads-up”opponent but also of the other players at the
table, because future decisions probably need to be made against them as well. Ostensibly, at an
“all female”table (unlike at “all male”and “gender mixed”tables) players do not need to worry
about having to face “strong male players”and being exploited by them. This hypothesis is
sensible, since the participants made decisions against every opponent; at the “gender mixed
table”, the male opponents were not “merely observers”.
Another possible explanation relates to lack of salience in gender information in the current
experiment. Evidence suggests that gender salience affects the strength of gender-conditioned
behavior. For example, Datta Gupta et al. [15] observed that participants behaved more com-
petitively against female co-players than against male ones, but only when they knew their co-
player's (female) nickname actually belonged to a female—i.e., when they were presented with
“strong”as opposed to “weak”gender information. In our experiment, opponent nicknames
were gender-neutral (“opponent 1–4”). Gender salience could be increased by employing gen-
der-specific nicknames, or by increasing the attractiveness of the avatars, which might elicit
stronger effects.
On the other hand, an interesting venue for future work is to evaluate ways for female ava-
tars to signal their competence to reduce being deceived. Although competence is typically
associated with masculine features [38], there are probably ways to decrease the perceived
“bluffability”of female avatars without having to swap gender—for example by having female
avatars with shorter hair (or no hair), or angrier expressions.
Participants were not explicitly told that a female avatar “belongs”to a female player,
because in online poker players rarely know their opponents' genders with certainty. Despite
uncertainty about player genders, the perceived gender of avatars has been shown to influence
behavior online. For example, female online poker avatars were observed receiving more inap-
propriate chat comments than their male counterparts [41]. Hussain and Griffiths [42] found
that some females believed they can avoid online harassment by assuming a male avatar in
role-playing games. Avatars alone can act as gender-cues and predispose players to behave in
a gender-stereotyping manner—although increasing the salience of gender information can
strengthen this effect.
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It is noteworthy that most participants did not believe the avatar genders influenced their
decisions, suggesting that the observed effect on bluffing was implicit. For many players, espe-
cially experienced ones, bluffing depends heavily on the opponents’previous betting “pat-
terns”. For example, an opponent who first bets a large amount pre-flop (cf. Glossary in S1
Supporting Information p. 3) but abruptly stops betting afterwards might seem suspicious due
to an “inconsistent”betting style. Putatively, the participants in the current study paid closer
attention to information about the opponents’betting patterns than their (avatar) genders.
Our results nonetheless demonstrate that “weak”opponent gender information is sufficient to
influence behavior, even when little conscious attention is paid to the avatars.
In terms of limitations, our study was Internet-based, and thus we were unable to control
for possible distractions. In line with the current results, previous studies [36,37,39] have dem-
onstrated that in online poker communities, at least 90% of the players are male. Because
females are underrepresented among poker players, they might be a selected group of players
who are more competitive than non-poker playing females. Given the significant gender imbal-
ance in our sample, the results on gender differences should be viewed with caution and con-
sidered as tentative, as they might not be generalizable. In terms of our main results, we are
limited to the conclusion that at least male players (but not necessarily females) bluff more fre-
quently at online poker tables with female avatar opponents than at tables with male ones.
Nonetheless, our sample was diverse in terms of education, income and age, which is often
not the case in psychological studies sampling student populations within a single university.
Finally, participants did not play an actual poker game or wage their own money, which
reduces ecological validity. However, because we used realistic visual poker scenarios emulating
the most popular online poker site (www.pokerstars.net), our setting was more ecologically
valid than others previously used successfully in online poker research [37]. We also received
positive comments from some of our participants about the tasks, saying our poker scenarios
felt “surprisingly”similar to actual online poker decisions.
In conclusion, the current study demonstrated gender effects in online poker bluffing,
which arguably stem from gender-stereotyped beliefs about male and female players’competi-
tive characteristics. Importantly, online poker is an environment where the features of players’
avatars likely have significant monetary consequences. In online poker, multiple betting deci-
sions involving real money are made within short periods of time, and many active players
have made millions of betting decisions in total [36]. Thus, if avatar gender can even marginally
influence decision-making, this effect will rapidly accumulate. In a similar vein, casinos typi-
cally exclude from their premises anyone who is able to obtain a marginal edge over them (e.g.,
a 0.5% edge in blackjack; [43]). An estimated difference of 6% in bluffing frequency, as we
observed, is thus highly significant. This is further supported by our expected monetary value
calculations, which suggested there is a difference of about $22 per bet in bluffing profitability
when playing at online poker tables comprising of all female avatars as opposed to all male
ones.
In general, poker has been studied only occasionally in psychology [44], and poker studies
focusing on deception are particularly scarce. We advocate that future research can gain much
from employing poker to study the multifaceted phenomenon of deception. For example, in
addition to bluffing, there are other forms of deception in poker. One of these is slow-playing
(or trapping), which is roughly the opposite of bluffing: betting weakly or not at all with a very
strong hand to “lure”the opponent into betting or raising with a hand they would normally
not play with (luring someone into a “trap”). Furthermore, a player who is known for bluffing
might play with a very strong hand, making it seem as if s/he were just bluffing again. This is
an example of “deceiving by telling the truth”and adds another layer to ways in which people
might deceive and manipulate each other in poker.
Gender Biased Deceptive Behavior in Online Poker
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These various types of deception can be conveniently investigated by using poker as an eco-
logically valid tool, but probably not otherwise. Such studies will likely reveal new insights on
deception in poker, a popular and pervasive game, and on the psychology of deception in gen-
eral. Therefore, poker is a useful addition to the common toolbox for psychology research.
Supporting Information
S1 Supporting Information. Multiple figures and tables, stimulus materials, and other
additional information (statistical analyses, other covariates unrelated to current aims,
data transparency table, and a glossary of poker terminology) in a single file.
(DOCX)
Acknowledgments
Part of the work for this study was done when Jussi Palomäki and Jeff Yan were affiliated with
School of Computing Science, Newcastle University, UK. We thank Professor Ross Anderson,
Dr. Jeunese Payne and Dr. Sophie van der Zee (Cambridge University), Professor Aldert Vrij
(University of Portsmouth), and Professor Michelle Baddeley (University College London)
for invaluable feedback on our manuscript. We also thank Mr. Abdalnaser Algwil (Lancaster
University) for helping with coding the experiment, and Mr. Tommi Verkasalo (professional
poker player) for invaluable help in the expected value calculations. Finally, we thank the
numerous online poker communities (most notably www.pokerisivut.com,www.pokeritieto.
com, and www.pokerstrategy.com) for their participation in our study.
Author Contributions
Conceived and designed the experiments: JP JY ML. Performed the experiments: JP. Analyzed
the data: JP. Wrote the paper: JP JY ML DM.
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