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Randomized Controlled Trial
. 2010 Jul 21;5(7):e11663.
doi: 10.1371/journal.pone.0011663.

Human Wagering Behavior Depends on Opponents' Faces

Free PMC article
Randomized Controlled Trial

Human Wagering Behavior Depends on Opponents' Faces

Erik J Schlicht et al. PLoS One. .
Free PMC article


Research in competitive games has exclusively focused on how opponent models are developed through previous outcomes and how peoples' decisions relate to normative predictions. Little is known about how rapid impressions of opponents operate and influence behavior in competitive economic situations, although such subjective impressions have been shown to influence cooperative decision-making. This study investigates whether an opponent's face influences players' wagering decisions in a zero-sum game with hidden information. Participants made risky choices in a simplified poker task while being presented opponents whose faces differentially correlated with subjective impressions of trust. Surprisingly, we find that threatening face information has little influence on wagering behavior, but faces relaying positive emotional characteristics impact peoples' decisions. Thus, people took significantly longer and made more mistakes against emotionally positive opponents. Differences in reaction times and percent correct were greatest around the optimal decision boundary, indicating that face information is predominantly used when making decisions during medium-value gambles. Mistakes against emotionally positive opponents resulted from increased folding rates, suggesting that participants may have believed that these opponents were betting with hands of greater value than other opponents. According to these results, the best "poker face" for bluffing may not be a neutral face, but rather a face that contains emotional correlates of trustworthiness. Moreover, it suggests that rapid impressions of an opponent play an important role in competitive games, especially when people have little or no experience with an opponent.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.


Figure 1
Figure 1. Diagram shows a Bayesian network for the poker task used in this experiment.
White nodes are hidden variables, and gray nodes correspond to observable information. Arrows between nodes represent conditional relationships between the variables. In this scenario, the probability of winning the bet amount is based on the subject's starting hand (observable variable) and their opponent's hand (hidden variable). Since subjects cannot directly observe their opponent's hand, they can use the fact that the opponent bet (observable variable) to put them on a ‘range’ of possible hands. However, in order to do this accurately, they must have an estimate of their opponent's style of play (hidden variable). More specifically, the probability of a particular hand winning is lower against an opponent who only bets with high-value hands, compared to an opponent who frequently bluffs (i.e, bets with poor hands). Since the opponent's style is also a hidden variable, subjects can use an opponent's face information (observable variable) to estimate their style. This process is called Bayesian ‘explaining away’, as the opponent's face explains-away the possibility of a bluff being the cause of an opponent's bet.
Figure 2
Figure 2. Diagram of the display viewed by participants in this experiment, and expected values for each of the two possible decisions.
[A] Participants played a simplified version of Texas Hold'em poker and were provided information about their starting hand and the opponent who was betting. Based on this information, they were required to make call/fold decisions. If participants choose to fold, they are guaranteed to lose their blind (−100 chips), whereas if they choose to call, they have a chance to either win or lose the bet amount (5000 chips) that is based on the probability of their hand winning against a random opponent. Opponent faces were obtained from an online database , . The right column of the figure shows one face identity for three different trustworthiness values. [B] Graph shows how the expected value for each decision changes across starting hands. The ‘optimal decision’ would be the one that results in the greatest expected value. Therefore, participants should fold when the probability of their hand winning is below .49, and call if it is greater. See the Experimental Methods Section for additional details.
Figure 3
Figure 3. Figure demonstrates changes in reaction times.
For Panel A, the first 14 bars reflect individual participant data, while the last bar represents the average for each condition (Error bars represent ± SEM). [A] Change in reaction time across face conditions. Participants took significantly longer to make a decision against a trustworthy opponent (Blue) than untrustworthy (Red) and Neutral (Green) opponents. [B] Mean change in reaction time across starting hand value. People took significantly longer (collapsing across trustworthiness conditions) to make decisions for hands in the optimal fold region (left of black dashed line) than hands in the optimal call region (right of dashed line). Moreover, differences between trustworthiness groups were most pronounced around the decision boundary.
Figure 4
Figure 4. Figure shows changes in correct decisions.
For Panel A, the first 14 bars reflect individual participant data, while the last bar represents the average for each condition (Error bars represent ± SEM). [A] Change in correct decisions across face types. Participants made significantly more mistakes against trustworthy opponents (Blue) than neutral (Green) and untrustworthy (Red) opponents. [B] Mean change in correct decisions across starting-hand value. People did significantly worse (collapsing across trustworthiness conditions) for hands near the optimal decision boundary. Differences between groups were also more pronounced for these mid-value hands.
Figure 5
Figure 5. Changes in calling behavior and loss aversion parameters across faces types.
In Panel A, the first 14 bars reflect individual participant data, while the last bar represents the average for each condition (Error bars represent ± SEM). [A] Change in calling decisions across face types. Participants called significantly less against trustworthy opponents (Blue) than neutral (Green) opponents. [B] The observed changes in calling resulted from a shift in the average calling function for trustworthy faces. This suggests that participants ded a staneerting hand with greater expected value in order to call at similar rates against a trustworthy opponent. [C] Change in lambda values for the utility fits across face conditions. The results show that lambda values are significantly greater against trustworthy opponents than against neutral or untrustworthy opponents. Moreover, subjects are gain-loss neutral, unless they are playing a trustworthy opponent, when they show significant loss aversion.

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