Temporal difference models describe higher-order learning in humans

Ben Seymour (), John P. O'Doherty, Peter Dayan, Martin Koltzenburg, Anthony K. Jones, Raymond J. Dolan, Karl J. Friston and Richard S. Frackowiak
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Ben Seymour: Wellcome Department of Imaging Neuroscience
John P. O'Doherty: Wellcome Department of Imaging Neuroscience
Peter Dayan: Gatsby Computational Neuroscience Unit, Alexandra House
Martin Koltzenburg: University College London
Anthony K. Jones: University of Manchester Rheumatic Diseases Centre, Hope Hospital
Raymond J. Dolan: Wellcome Department of Imaging Neuroscience
Karl J. Friston: Wellcome Department of Imaging Neuroscience
Richard S. Frackowiak: Wellcome Department of Imaging Neuroscience

Nature, 2004, vol. 429, issue 6992, 664-667

Abstract: Abstract The ability to use environmental stimuli to predict impending harm is critical for survival. Such predictions should be available as early as they are reliable. In pavlovian conditioning, chains of successively earlier predictors are studied in terms of higher-order relationships, and have inspired computational theories such as temporal difference learning1. However, there is at present no adequate neurobiological account of how this learning occurs. Here, in a functional magnetic resonance imaging (fMRI) study of higher-order aversive conditioning, we describe a key computational strategy that humans use to learn predictions about pain. We show that neural activity in the ventral striatum and the anterior insula displays a marked correspondence to the signals for sequential learning predicted by temporal difference models. This result reveals a flexible aversive learning process ideally suited to the changing and uncertain nature of real-world environments. Taken with existing data on reward learning2, our results suggest a critical role for the ventral striatum in integrating complex appetitive and aversive predictions to coordinate behaviour.

Date: 2004
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DOI: 10.1038/nature02581

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