Theoretical and empirical studies of learning

Yael Niv, P. Read Montague

Research output: Chapter in Book/Report/Conference proceedingChapter

24 Scopus citations


This chapter introduces the reinforcement learning framework and gives a brief background to the origins and history of reinforcement learning models of decision-making. Reinforcement learning provides a normative framework, within which conditioning can be analyzed. That is, this suggests a means by which optimal prediction and action selection can be achieved, and exposes explicitly the computations that must be realized in the service of these. In contrast to descriptive models that describe behavior as it is, normative models study behavior from the point of view of its hypothesized function-that is, they study behavior, as it should be if it were to accomplish specific goals in an optimal way. The appeal of normative models derives from several sources. Historically, the core ideas in reinforcement learning arose from two separate and parallel lines of research. One axis is mainly associated with Richard Sutton, formerly an undergraduate psychology major, and his PhD advisor, Andrew Barto, a computer scientist. Interested in artificial intelligence and agent-based learning, Sutton and Barto developed algorithms for reinforcement learning that were inspired by the psychological literature on Pavlovian and instrumental conditioning.

Original languageEnglish (US)
Title of host publicationNeuroeconomics
PublisherElsevier Inc.
Number of pages21
ISBN (Print)9780123741769
StatePublished - 2009

All Science Journal Classification (ASJC) codes

  • General Neuroscience


Dive into the research topics of 'Theoretical and empirical studies of learning'. Together they form a unique fingerprint.

Cite this