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Temporal difference models and reward-related learning in the human brain.

John P O'Doherty1, Peter Dayan, Karl Friston

  • 1Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, WC1N 3BG, London, United Kingdom. j.odoherty@fil.ion.ucl.ac.uk

Neuron
|April 30, 2003
PubMed
Summary

Temporal difference learning models Pavlovian conditioning. Brain imaging reveals ventral striatum and orbitofrontal cortex show prediction error signals during appetitive conditioning.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Cognitive Science

Background:

  • Pavlovian conditioning involves learning associations between stimuli and rewards.
  • Temporal difference (TD) learning is a computational model for associative learning.
  • A key feature of TD learning is the prediction error signal, which updates value estimates.

Purpose of the Study:

  • To identify brain regions exhibiting the temporal difference learning prediction error signal profile during appetitive conditioning.
  • To investigate the neural basis of reward prediction in humans using functional magnetic resonance imaging (fMRI).

Main Methods:

  • Event-related fMRI was used to scan subjects during an appetitive conditioning task.
  • Subjects received a pleasant taste reward following a conditioned stimulus (CS).

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  • Regression analyses were performed to correlate brain activity with the TD learning prediction error signal.

    • Main Results:

    • Responses in the ventral striatum significantly correlated with the prediction error signal.
    • Activity in the orbitofrontal cortex also showed a significant correlation with the prediction error signal.
    • The observed signal profile shifted from reward onset (before learning) to CS onset (after learning).

    Conclusions:

    • The ventral striatum and orbitofrontal cortex are key brain regions involved in reward prediction during appetitive conditioning.
    • These findings support the application of temporal difference learning as a model for understanding human associative learning.
    • The study demonstrates the neural expression of TD learning computations in the human brain.