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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

Probabilistic relationship between auditory and visual stimuli. Contingency tables showing the proportion of each trial type occurring during CS+ and CS− blocks respectively. Below the tables are the resulting conditional probabilities of the visual stimulus being present (or absent), given the presence (or absence) of the auditory CS; these probabilities can be inferred by comparing the frequencies within each column of the table.
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fig2: Probabilistic relationship between auditory and visual stimuli. Contingency tables showing the proportion of each trial type occurring during CS+ and CS− blocks respectively. Below the tables are the resulting conditional probabilities of the visual stimulus being present (or absent), given the presence (or absence) of the auditory CS; these probabilities can be inferred by comparing the frequencies within each column of the table.

Mentions: In this study we used a factorial design that extended the first stage of classical sensory preconditioning paradigms. Healthy volunteers performed an audio-visual target-detection task, while being exposed to a stream of concurrent audio-visual “distractor” stimuli (Fig. 1). These stimuli possessed statistical regularities, which enabled prediction of the visual distractor from the preceding auditory cue (Fig. 2). Critically, however, these statistical associations were completely irrelevant to the target-detection task. Any learning of these associations would therefore be of an incidental (task-unrelated) nature and, in the absence of behavioral responses to the learned associations, could only be inferred neurophysiologically. This paradigm capitalized on previous work by McIntosh et al. (McIntosh et al. 1998) who used positron emission tomography (PET) to show that learning of associations between sensory stimuli was reflected by activity in early visual cortex. However, the use of PET permitted only a simple conditioning scheme and precluded a full investigation of dynamic changes in the brain's representation of the learned association. Here, we employed a more refined conditioning scheme and used functional magnetic resonance imaging (fMRI) to study learning-dependent changes in brain activity over time. Additionally, we assessed learning-dependent changes in effective connectivity between auditory and visual cortex using dynamic causal modeling (DCM).


A dual role for prediction error in associative learning.

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

Probabilistic relationship between auditory and visual stimuli. Contingency tables showing the proportion of each trial type occurring during CS+ and CS− blocks respectively. Below the tables are the resulting conditional probabilities of the visual stimulus being present (or absent), given the presence (or absence) of the auditory CS; these probabilities can be inferred by comparing the frequencies within each column of the table.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Probabilistic relationship between auditory and visual stimuli. Contingency tables showing the proportion of each trial type occurring during CS+ and CS− blocks respectively. Below the tables are the resulting conditional probabilities of the visual stimulus being present (or absent), given the presence (or absence) of the auditory CS; these probabilities can be inferred by comparing the frequencies within each column of the table.
Mentions: In this study we used a factorial design that extended the first stage of classical sensory preconditioning paradigms. Healthy volunteers performed an audio-visual target-detection task, while being exposed to a stream of concurrent audio-visual “distractor” stimuli (Fig. 1). These stimuli possessed statistical regularities, which enabled prediction of the visual distractor from the preceding auditory cue (Fig. 2). Critically, however, these statistical associations were completely irrelevant to the target-detection task. Any learning of these associations would therefore be of an incidental (task-unrelated) nature and, in the absence of behavioral responses to the learned associations, could only be inferred neurophysiologically. This paradigm capitalized on previous work by McIntosh et al. (McIntosh et al. 1998) who used positron emission tomography (PET) to show that learning of associations between sensory stimuli was reflected by activity in early visual cortex. However, the use of PET permitted only a simple conditioning scheme and precluded a full investigation of dynamic changes in the brain's representation of the learned association. Here, we employed a more refined conditioning scheme and used functional magnetic resonance imaging (fMRI) to study learning-dependent changes in brain activity over time. Additionally, we assessed learning-dependent changes in effective connectivity between auditory and visual cortex using dynamic causal modeling (DCM).

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