A Unifying Probabilistic View of Associative Learning

Samuel J Gershman

PLOS Computational Biology, 2015, vol. 11, issue 11, 1-20

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.Author Summary: How do we learn about associations between events? The seminal Rescorla-Wagner model provided a simple yet powerful foundation for understanding associative learning. However, much subsequent research has uncovered fundamental limitations of the Rescorla-Wagner model. One response to these limitations has been to rethink associative learning from a normative statistical perspective: How would an ideal agent learn about associations? First, an agent should track its uncertainty using Bayesian principles. Second, an agent should learn about long-term (not just immediate) reward, using reinforcement learning principles. This article brings together these principles into a single framework and shows how they synergistically account for a number of complex learning phenomena.

Date: 2015
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:plo:pcbi00:1004567

DOI: 10.1371/journal.pcbi.1004567

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