Header

UZH-Logo

Maintenance Infos

A dual role for prediction error in associative learning


den Ouden, H E M; Friston, K J; Daw, N D; McIntosh, A R; Stephan, K E (2009). A dual role for prediction error in associative learning. Cerebral Cortex, 19(5):1175-1185.

Abstract

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 learningdependent
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.

Abstract

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 learningdependent
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.

Statistics

Citations

131 citations in Web of Science®
136 citations in Scopus®
Google Scholar™

Altmetrics

Downloads

67 downloads since deposited on 27 Feb 2009
15 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Economics
Dewey Decimal Classification:330 Economics
Language:English
Date:May 2009
Deposited On:27 Feb 2009 07:36
Last Modified:03 Aug 2017 15:02
Publisher:Oxford University Press
ISSN:1047-3211
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.
Free access at:PubMed ID. An embargo period may apply.
Publisher DOI:https://doi.org/10.1093/cercor/bhn161
PubMed ID:18820290

Download

Preview Icon on Download
Preview
Filetype: PDF (Original publication)
Size: 1MB
View at publisher