{"pk":29703,"title":"Devaluation of Unchosen Options: A Bayesian Account of the Provenance andMaintenance of Overly Optimistic Expectations","subtitle":null,"abstract":"Humans frequently overestimate the likelihood of desirableevents while underestimating the likelihood of undesirableones: a phenomenon known as unrealistic optimism. Previ-ously, it was suggested that unrealistic optimism arises fromasymmetric belief updating, with a relatively reduced codingof undesirable information. Prior studies have shown that areinforcement learning (RL) model with asymmetric learningrates (greater for a positive prediction error than a negativeprediction error) could account for unrealistic optimism in abandit task, in particular the tendency of human subjects topersistently choosing a single option when there are multi-ple equally good options. Here, we propose an alternativeexplanation of such persistent behavior, by modeling humanbehavior using a Bayesian hidden Markov model, the Dy-namic Belief Model (DBM). We find that DBM captures hu-man choice behavior better than the previously proposed asym-metric RL model. Whereas asymmetric RL attains a measureof optimism by giving better-than-expected outcomes higherlearning weights compared to worse-than-expected outcomes,DBM does so by progressively devaluing the unchosen op-tions, thus placing a greater emphasis on choice history inde-pendent of reward outcome (e.g. an oft-chosen option mightcontinue to be preferred even if it has not been particularly re-warding), which has broadly been shown to underlie sequentialeffects in a variety of behavioral settings. Moreover, previouswork showed that the devaluation of unchosen options in DBMhelps to compensate for a default assumption of environmentalnon-stationarity, thus allowing the decision-maker to both bemore adaptive in changing environments and still obtain near-optimal performance in stationary environments. Thus, thecurrent work suggests both a novel rationale and mechanismfor persistent behavior in bandit tasks.","language":"eng","license":{"name":"","short_name":"","text":null,"url":""},"keywords":[{"word":"unrealistic optimism; decision making; multi-armed bandit; reinforcement learning; Bayesian modeling"}],"section":"Poster Session 1","is_remote":true,"remote_url":"https://escholarship.org/uc/item/4jj2g5w1","frozenauthors":[{"first_name":"Corey","middle_name":"Yishan","last_name":"Zhou","name_suffix":"","institution":"University of California, San Diego","department":""},{"first_name":"Dalin","middle_name":"","last_name":"Guo","name_suffix":"","institution":"University of California, San Diego","department":""},{"first_name":"Angela","middle_name":"J.","last_name":"Yu","name_suffix":"","institution":"University of California, San Diego","department":""}],"date_submitted":null,"date_accepted":null,"date_published":"2020-01-01T18:00:00Z","render_galley":null,"galleys":[{"label":"PDF","type":"pdf","path":"https://journalpub.escholarship.org/cognitivesciencesociety/article/29703/galley/19560/download/"}]}