A Unifying Probabilistic View of Associative Learning.

• Main Author: Samuel J Gershman
• Format: Article
• Language: English
• Published: Public Library of Science (PLoS) 2015-11-01
• Series: PLoS Computational Biology
• Online Access: http://europepmc.org/articles/PMC4633133?pdf=render

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.