More than One Kind of Probability Matching: Evidence from a Dual-Task Paradigm

More than One Kind of Probability Matching: Evidence from a Dual-Task Paradigm A. Ross Otto and Arthur B. Markman (rotto@mail.utexas.edu, markman@psy.utexas.edu) Department of Psychology, University of Texas, Austin, TX 78712 USA Eric G. Taylor (etaylor4@illinois.edu) Department of Psychology, University of Illinois, Champaign, IL 61820 USA Abstract result of integrating a moving window of past outcome infor- mation (Sugrue, Corrado, & Newsome, 2004). To generate a response, the individual stochastically and independently generates predictions in accordance with this historical as- sessment of outcome probabilities. Assuming a sufficiently long historical window, a decision-maker utilizing the EM strategy in the example above would stochastically allocate 65% of their predictions to Event A and 35% of their predic- tions to Event B. Probability-matching is a well-documented suboptimal behav- ior that arises in simple prediction tasks. We identify two dis- tinct, local choice strategies that both give rise to probability- matching behavior on a global level. Using a dual-task paradigm, we evaluate the hypothesis that these qualitatively different strategies exhibit different demands on individuals’ central executive resources. We find that participants placed under a concurrent working memory are driven away from the one-trial-back strategy—utilized by participants without a working memory load—and towards a strategy that integrates a longer window of past outcomes into the current prediction. In other words, the demands of the concurrent task appeared to shift the prediction strategies used by decision-makers in our study. Keywords: Decision-making; Prediction; Win-Stay-Lose- Shift; Working Memory; Dual Task; Heuristics Introduction One decision-making anomaly of great interest is the ten- dency for humans to match their responses to outcome prob- abilities in the prediction of binary outcomes. For example consider a laboratory task in which people need to repeatedly predict which of two outcomes (say Event A and Event B) will occur next. If Event A occurs at a base rate of p = . 65, Event B occurs at a base rate of p = . 35 and each outcome is conditionally independent of the last outcome, the optimal prediction strategy would be to always predict that Event A will occur next, which is called maximizing. However, a large body of empirical work suggests that people appear to predict events in proportion to their frequency of occurrence, known as probability matching (Estes, 1961; Vulkan, 2000). Under probability matching, a person would predict Event A 65% of the time and Event B 35% of the time. It is easy to see that this strategy produces an expected overall accuracy of 54.5% (calculated as .65 x .65 + .35 x .35), which is inferior to that produced by maximizing—which produces an expected over- all prediction accuracy of 65%. In the present study, we ex- amine strategies that be may underlying probability matching in random sequences of events. The psychological mechanisms that give rise to probabil- ity matching behavior are unclear and are a matter of ongo- ing debate. One hypothesis posits that probability match- ing arises from the use of a suboptimal cognitive shortcut in which individuals allocates their responses according to an assessment of the observed outcome probabilities (e.g., Koehler & James, 2009). Under this strategy, termed expec- tation matching (EM), the decision-maker’s responses are the Another proposal suggests that probability matching be- havior seen at a more global level is the byproduct of a lo- cal decision process called win-stay lose-shift (WSLS: Her- rnstein, Rachlin, & Laibson, 2000). Under WSLS, an indi- vidual persists with predicting one event, say Event A, until they make an incorrect prediction, at which point they shift responses and persist with predicting Event B until they are incorrect. While under certain task circumstances WSLS is an optimal choice strategy (Shimp, 1976), it is a subopti- mal prediction strategy in the task outlined above. It can be shown that WSLS produces overall response rates (and hence, accuracy rates) equivalent to probability matching (Unturbe & Corominas, 2007). Further, there is evidence that people utilize WSLS in the simple binary prediction task described above (Gaissmaier & Schooler, 2008). Unlike the EM strat- egy, which involves integrating a comparatively long histor- ical window of outcomes, WSLS requires that the decision- maker maintain a short-term memory for only the most recent response and outcome. In the present study, we examined the cognitive demands imposed by the WSLS and EM strategies, with the idea that decision makers may utilize both strategies, but under dif- ferent circumstances. While both strategies result in equiva- lent behavior at a global level—probability matching—they make different behavioral predictions at a local, trial-by-trial level. It is well documented that the working memory de- mands of a secondary task deplete mental resources that could otherwise be used to accomplish a primary task (Pashler, 1994). For example, Zeithamova and Maddox (2006) found that working memory load disrupts learning of explicit, rule- based categories and drives participants towards the use of an implicit, information-integration strategy. Here, we place decision-makers under a concurrent working memory load and find that they exhibit the same global tendency to proba- bility match as decision-makers without a working memory