Emotional and Social Factors influence Poker Decision Making Accuracy

Abstract

Poker is a social game, where success depends on both game strategic knowledge and emotion regulation abilities. Thus, poker provides a productive environment for studying the effects of emotional and social factors on micro-economic decision making. Previous research indicates that experiencing negative emotions, such as moral anger, reduces mathematical accuracy in poker decision making. Furthermore, various social aspects of the game—such as losing against "bad players" due to "bad luck"—seem to fuel these emotional states. We designed an Internet-based experiment, where participants' (N = 459) mathematical accuracy in five different poker decision making tasks were assessed. In addition, we manipulated the emotional and social conditions under which the tasks were presented, in a 2 × 2 experimental setup: (1) Anger versus neutral emotional state—participants were primed either with an anger-inducing, or emotionally neutral story, and (2) Social cue versus non-social cue—during the tasks, either an image of a pair of human eyes was "following" the mouse cursor, or an image of a black moving box was presented. The results showed that anger reduced mathematical accuracy of decision making only when participants were "being watched" by a pair of moving eyes. Experienced poker players made mathematically more accurate decisions than inexperienced ones. The results contribute to current understanding on how emotional and social factors influence decision making accuracy in economic games.

Behavioural and neural limits in competitive decision making: The roles of outcome, opponency and observation

“…Therefore, in the context of strategic opponency, the removal of response choice had an inhibitory effect on performance. Secondly and in alignment with previous results, participants were more likely to engage in higher-quality decision making following a positive outcome (win) relative to a negative outcome (lose or draw; Dyson et al, 2016;Forder & Dyson, 2016;Laakasuo et al, 2015;Mitzenmacher & Upfal, 2005). This was despite the fact that the strategy itself was a form of shift behaviour.…”

“…Part of the reason why sub-optimal, exploitable lose-shift behaviour reliably occurs may be due to the self-imposed reduction in processing time allocated to decision making on trials following negative outcome (Dixon & Schreiber, 2004;Dyson, Sundvall, Forder & Douglas, 2018;Forder & Dyson, 2016;Verbruggen, Chambers, Lawrence & McLaren, 2017). Such ideas are also consistent with observations of tilting behaviour following loss in poker (e.g., Laakasuo et al, 2015), chasing behaviour following loss in roulette (e.g., Mitzenmacher & Upfal, 2005), and post-reinforcement pauses where gamblers revel in or 'consume' the current reward such that the initiation of the next trial takes longer following positive outcome (Dixon, MacLaren, Jarick, Fugelsang & Harrigan, 2013; see also Zheng et al, 2017).…”

Poker players with experience and skill are not “ill” – Exposing a discrepancy in measures of problem gambling

“…They were offered the possibility of taking part in a draw of four separate $50 gift coupons to Amazon.com. The data were collected prior to inviting participants to engage an experimental paradigm (completed online) assessing poker decision-making accuracy, the results of which have been previously published (see Laakasuo et al, 2014).…”

Behavioural and neural interactions between objective and subjective performance in a Matching Pennies game

“…In contrast to item and contingency biases, combination biases also showed an interaction between the degree of randomness deviation and condition, where participants were closer to expressing randomness with respect to response pairs (i.e., HH, HT, TH, TT) within a condition where wins were more likely than losses. We take this as further evidence that higher-quality decision-making is more likely when exposed to a context containing more positive than negative outcomes Laakasuo et al, 2015;Mitzenmacher & Upfal, 2005). We also observed a second effect where increased exposure to wins lead to more accurate performance.…”

“…If deviations from randomness and sub-optimal System 1-style behaviour are in part predicted by the experience of negative relative to positive outcomes Laakasuo et al, 2015;Mitzenmacher & Upfal, 2005), then a critical component in the expression of higher-or poorer-quality behaviour becomes the relationship between actual and perceived performance. Here, a final form of bias is that participants exhibit over-optimism regarding their own performance (Cazé & van der Meer, 2013).…”