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A dual role for prediction error in associative learning.

den Ouden HE, Friston KJ, Daw ND, McIntosh AR, Stephan KE - Cereb. Cortex (2008)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. h.denouden@fil.ion.ucl.ac.uk

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 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.

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Related in: MedlinePlus

Compound learning curves. Learning curves were calculated separately for trials on which the auditory CS was present (dots) and absent (crosses), during CS+ (blue), and CS− (red) blocks. Note that learning is slower in the absence of an auditory CS than in its presence and faster for CS+ than for CS− trials.
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fig3: Compound learning curves. Learning curves were calculated separately for trials on which the auditory CS was present (dots) and absent (crosses), during CS+ (blue), and CS− (red) blocks. Note that learning is slower in the absence of an auditory CS than in its presence and faster for CS+ than for CS− trials.

Mentions: Because of its short duration and small size, the TO cue is less salient than the CS. Because in the RW model the learning rate reflects stimulus properties including salience (Rescorla and Wagner 1972), ϵTO can be assumed to be considerably smaller than ϵCS. In this study ϵTO was assumed to be 4 times smaller than the ϵCS. It should be noted that violations of this assumption are unlikely to have a dramatic effect because the inclusion of the derivatives enables the model to cope with deviations from the assumed learning rates (see above). The resulting learning curves are shown in Figure 3 (see Supplementary Fig. 1A for a breakdown of the learning curves with regard to the 2 cue components).


A dual role for prediction error in associative learning.

den Ouden HE, Friston KJ, Daw ND, McIntosh AR, Stephan KE - Cereb. Cortex (2008)

Compound learning curves. Learning curves were calculated separately for trials on which the auditory CS was present (dots) and absent (crosses), during CS+ (blue), and CS− (red) blocks. Note that learning is slower in the absence of an auditory CS than in its presence and faster for CS+ than for CS− trials.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2665159&req=5

fig3: Compound learning curves. Learning curves were calculated separately for trials on which the auditory CS was present (dots) and absent (crosses), during CS+ (blue), and CS− (red) blocks. Note that learning is slower in the absence of an auditory CS than in its presence and faster for CS+ than for CS− trials.
Mentions: Because of its short duration and small size, the TO cue is less salient than the CS. Because in the RW model the learning rate reflects stimulus properties including salience (Rescorla and Wagner 1972), ϵTO can be assumed to be considerably smaller than ϵCS. In this study ϵTO was assumed to be 4 times smaller than the ϵCS. It should be noted that violations of this assumption are unlikely to have a dramatic effect because the inclusion of the derivatives enables the model to cope with deviations from the assumed learning rates (see above). The resulting learning curves are shown in Figure 3 (see Supplementary Fig. 1A for a breakdown of the learning curves with regard to the 2 cue components).

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London WC1N 3BG, UK. h.denouden@fil.ion.ucl.ac.uk

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 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.

Show MeSH
Related in: MedlinePlus