TY - JOUR
T1 - A dual role for prediction error in associative learning
AU - Den Ouden, Hanneke E M
AU - Friston, Karl J.
AU - Daw, Nathaniel D.
AU - McIntosh, Anthony R.
AU - Stephan, Klaas E.
N1 - Funding Information:
Wellcome Trust (ref: 0856780/Z/99/B); Wellcome Trust PhD studentship (ref: 078047/ZS/04/Z) supported H.D.O.; and University Research Priority Program ‘‘Foundations of Human Social Interactions’’ at the University of Zurich supported K.E.S.
PY - 2009/5
Y1 - 2009/5
N2 - 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 modeling (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 modeled 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.
AB - 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 modeling (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 modeled 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.
KW - Associative learning
KW - Cross-modal
KW - Dynamic causal modeling
KW - Effective connectivity
KW - FMRI
KW - Rescorla-Wagner model
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UR - http://www.scopus.com/inward/citedby.url?scp=67649185192&partnerID=8YFLogxK
U2 - 10.1093/cercor/bhn161
DO - 10.1093/cercor/bhn161
M3 - Article
C2 - 18820290
AN - SCOPUS:67649185192
SN - 1047-3211
VL - 19
SP - 1175
EP - 1185
JO - Cerebral Cortex
JF - Cerebral Cortex
IS - 5
ER -