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Type Journal Article
Scope Discipline-based scholarship
Title A dual role for prediction error in associative learning
Organization Unit
Authors
  • H E M den Ouden
  • K J Friston
  • N D Daw
  • A R McIntosh
  • Klaas Enno Stephan
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title Cerebral Cortex
Publisher Oxford University Press
Geographical Reach international
ISSN 1047-3211
Volume 19
Number 5
Page Range 1175 - 1185
Date 2009
Abstract Text Confronted with a rich sensory environment, the brain must learn statistical regularities across sensory domains to construct causal models of the world. Here, we used functional magnetic resonance imaging and dynamic causal modelling (DCM) to furnish neurophysiological evidence that statistical associations are learnt, even when task-irrelevant. Subjects performed an audio-visual target detection task while being exposed to distractor stimuli. Unknown to them, auditory distractors predicted the presence or absence of subsequent visual distractors. We modelled incidental learning of these associations using a Rescorla-Wagner (RW) model. Activity in primary visual cortex and putamen reflected learning-dependent surprise: these areas responded progressively more to unpredicted, and progressively less to predicted, visual stimuli. Critically, this prediction-error response was observed even when the absence of a visual stimulus was surprising. We investigated the underlying mechanism by embedding the RW model into a DCM to show that auditory-to-visual connectivity changed significantly over time as a function of prediction error. Thus, consistent with predictive coding models of perception, associative learning is mediated by prediction-error dependent changes in connectivity. These results posit a dual role for prediction-error in encoding surprise and driving associative plasticity.
Free access at PubMed ID
Digital Object Identifier 10.1093/cercor/bhn161
PubMed ID 18820290
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Additional Information This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.