Gershman, Samuel2015-12-042015Gershman, Samuel J. 2015. “A Unifying Probabilistic View of Associative Learning.” PLoS Computational Biology 11 (11): e1004567. doi:10.1371/journal.pcbi.1004567. http://dx.doi.org/10.1371/journal.pcbi.1004567.1553-734Xhttp://nrs.harvard.edu/urn-3:HUL.InstRepos:23845336Two 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.en-USJournal Article2015-12-0410.1371/journal.pcbi.1004567