title: A Unifying Probabilistic View of Associative Learning.
abstract: Two important ideas about associative learning have emerged in recent decades: (1) Animals are Bayesian learners, tracking their uncertainty about associations; and (2) animals acquire long-term reward predictions through reinforcement learning. Both of these ideas are normative, in the sense that they are derived from rational design principles. They are also descriptive, capturing a wide range of empirical phenomena that troubled earlier theories. This article describes a unifying framework encompassing Bayesian and reinforcement learning theories of associative learning. Each perspective captures a different aspect of associative learning, and their synthesis offers insight into phenomena that neither perspective can explain on its own.
authors.affiliation: Department of Psychology and Center for Brain Science, Harvard University, Cambridge, Massachusetts, United States of America.
authors.forename: Samuel J
authors.initials: SJ
authors.lastname: Gershman
mesh_terms: D000465:Algorithms; D000818:Animals; D001245:Association Learning / Q000502:physiology*; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D008959:Models, Neurological