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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

Experimental design. (A) stimuli presented during the experiment. The “distractor” stimuli, whose associations are being learned incidentally, comprised 2 auditory CS corresponding to high- and low-frequency tones and one visual US consisting of 3 concentric squares. The target stimuli, to which the subjects responded, comprised a white noise burst and a circle. (B) Temporal sequence of a single trial. The CS and US could be either presented or omitted. The average trial duration was 2 s. The TO cue was a small central dot (100 ms); the auditory CS was presented for 500 ms, starting 400 ms after TO. The visual stimulus was presented 750 ms after TO, also for 500 ms. The intertrial interval (ITI) was jittered, ranging from 350–1350 ms, and target stimuli were inserted only in the longest ITIs, lasting for 300 ms.
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fig1: Experimental design. (A) stimuli presented during the experiment. The “distractor” stimuli, whose associations are being learned incidentally, comprised 2 auditory CS corresponding to high- and low-frequency tones and one visual US consisting of 3 concentric squares. The target stimuli, to which the subjects responded, comprised a white noise burst and a circle. (B) Temporal sequence of a single trial. The CS and US could be either presented or omitted. The average trial duration was 2 s. The TO cue was a small central dot (100 ms); the auditory CS was presented for 500 ms, starting 400 ms after TO. The visual stimulus was presented 750 ms after TO, also for 500 ms. The intertrial interval (ITI) was jittered, ranging from 350–1350 ms, and target stimuli were inserted only in the longest ITIs, lasting for 300 ms.

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)

Experimental design. (A) stimuli presented during the experiment. The “distractor” stimuli, whose associations are being learned incidentally, comprised 2 auditory CS corresponding to high- and low-frequency tones and one visual US consisting of 3 concentric squares. The target stimuli, to which the subjects responded, comprised a white noise burst and a circle. (B) Temporal sequence of a single trial. The CS and US could be either presented or omitted. The average trial duration was 2 s. The TO cue was a small central dot (100 ms); the auditory CS was presented for 500 ms, starting 400 ms after TO. The visual stimulus was presented 750 ms after TO, also for 500 ms. The intertrial interval (ITI) was jittered, ranging from 350–1350 ms, and target stimuli were inserted only in the longest ITIs, lasting for 300 ms.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig1: Experimental design. (A) stimuli presented during the experiment. The “distractor” stimuli, whose associations are being learned incidentally, comprised 2 auditory CS corresponding to high- and low-frequency tones and one visual US consisting of 3 concentric squares. The target stimuli, to which the subjects responded, comprised a white noise burst and a circle. (B) Temporal sequence of a single trial. The CS and US could be either presented or omitted. The average trial duration was 2 s. The TO cue was a small central dot (100 ms); the auditory CS was presented for 500 ms, starting 400 ms after TO. The visual stimulus was presented 750 ms after TO, also for 500 ms. The intertrial interval (ITI) was jittered, ranging from 350–1350 ms, and target stimuli were inserted only in the longest ITIs, lasting for 300 ms.
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