title: Reinforcement learning and counterfactual reasoning explain adaptive behavior in a changing environment.
abstract: Animals routinely adapt to changes in the environment in order to survive. Though reinforcement learning may play a role in such adaptation, it is not clear that it is the only mechanism involved, as it is not well suited to producing rapid, relatively immediate changes in strategies in response to environmental changes. This research proposes that counterfactual reasoning might be an additional mechanism that facilitates change detection. An experiment is conducted in which a task state changes over time and the participants had to detect the changes in order to perform well and gain monetary rewards. A cognitive model is constructed that incorporates reinforcement learning with counterfactual reasoning to help quickly adjust the utility of task strategies in response to changes. The results show that the model can accurately explain human data and that counterfactual reasoning is key to reproducing the various effects observed in this change detection paradigm.
authors.affiliation: Department of Computer and Information Science, University of Oregon.
authors.forename: Yunfeng
authors.initials: Y
authors.lastname: Zhang
authors.forename: Jaehyon
authors.initials: J
authors.lastname: Paik
authors.forename: Peter
authors.initials: P
authors.lastname: Pirolli
mesh_terms: D000223:Adaptation, Psychological / Q000502:physiology*; D000328:Adult; D004777:Environment; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D012054:Reinforcement, Psychology; D012201:Reward; D013850:Thinking / Q000502:physiology*; D055815:Young Adult
keywords: Change detection; Cognitive modeling; Counterfactual reasoning; Optimal foraging; Reinforcement learning