TY - JOUR T1 - Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis JF - bioRxiv DO - 10.1101/104752 SP - 104752 AU - Carolina Feher da Silva AU - Camila Gomes Victorino AU - Nestor Caticha AU - Marcus Vinícius Chrysóstomo Baldo Y1 - 2017/01/01 UR - http://biorxiv.org/content/early/2017/05/11/104752.abstract N2 - Research has not yet reached a consensus on why human participants perform suboptimally and match probabilities instead of maximize in a probability learning task. The most influential explanation is that participants search for patterns in the random sequence of outcomes. Other explanations, such as expectation matching, are plausible, but do not take into account how reinforcement learning shapes people’s choices.This study aimed to quantify how human performance in a probability learning task is affected by pattern search and reinforcement learning. We collected behavioral data from 84 young adult participants who performed a probability learning task wherein the most frequent outcome was rewarded with 0.7 probability. We then analyzed the data using a reinforcement learning model that searches for patterns. Model simulations indicated that pattern search, exploration (making random choices to learn more about the environment), recency (discounting early experiences to account for a changing environment), and forgetting may impair performance in a probability learning task.Our analysis estimated that 85% (95% HDI [76, 94]) of participants searched for patterns and believed that each trial outcome depended on one or two previous ones. The estimated impact of pattern search on performance was, however, only 6%, while those of exploration and recency were 19% and 13% respectively. This suggests that probability matching is caused by uncertainty about how outcomes are generated, which leads to pattern search, exploration, and recency. ER -