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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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Learning effects on audio-visual connectivity. Bayesian model comparison showed that the DCM with a single connection from A1 to V1 was superior to the other models. Across subjects, there was a significant “endogenous” or “fixed” strength of the A1 → V1 connection (0.10 s−1, P = 0.003) and a significant learning-induced modulation (magenta arrows) of this connection (P = 0.028). The insets show the parameter estimates for the main effects in both A1 and peripheral V1. The magenta arrows indicate how the main effect in peripheral V1 is modulated by changes in connectivity from A1 to V1 during CS+ trials: over time the response to surprising visual outcomes is upregulated, whereas the response to unsurprising visual outcomes is downregulated. Note that in this plot the magenta arrows designate the direction in which V1 responses change due to modulation of connectivity; for quantitative information on this modulatory effect, see the main text.
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fig6: Learning effects on audio-visual connectivity. Bayesian model comparison showed that the DCM with a single connection from A1 to V1 was superior to the other models. Across subjects, there was a significant “endogenous” or “fixed” strength of the A1 → V1 connection (0.10 s−1, P = 0.003) and a significant learning-induced modulation (magenta arrows) of this connection (P = 0.028). The insets show the parameter estimates for the main effects in both A1 and peripheral V1. The magenta arrows indicate how the main effect in peripheral V1 is modulated by changes in connectivity from A1 to V1 during CS+ trials: over time the response to surprising visual outcomes is upregulated, whereas the response to unsurprising visual outcomes is downregulated. Note that in this plot the magenta arrows designate the direction in which V1 responses change due to modulation of connectivity; for quantitative information on this modulatory effect, see the main text.

Mentions: Because the learning effect was mainly driven under CS+ blocks, we focused on changes in connectivity between auditory and visual cortices during incidental learning of the predictive attributes of CS+ trials (see Fig. 6). Bayesian model comparison showed that a DCM with a single connection from A1 to V1 (model 1) was superior to alternative models with reciprocal connections (group Bayes factor in favor of model 1: 2.1 × 1017 and 2.2 × 1018 when compared with model 2 and model 3, respectively). Across subjects, the A1 → V1 connection in the optimum model had an average strength of 0.10 s−1 (p = 0.003, df = 13, t = 3.57). During CS+ trials, this connection was significantly modulated by learning, depending on whether the visual stimulus was present or not (i.e., CS+ × (V+ vs. V−) × ϕ in Fig. 6). Note that the modulatory variable in the DCM corresponds to the interaction of the auditory prediction with the visual outcome during CS+ trials. It accounts for a learning-dependent contribution from CS+ responses in auditory cortex to visual cortex responses that depends on whether the visual stimulus was present or not (c.f., a prediction error mediated by top-down signals from auditory areas). Quantitatively, the strength of this modulation was −0.01 s−1 (p = 0.028, df = 13, t = 2.49). This corresponds to learning-induced changes in connectivity ranging from 2% (for CS+A− trials) to 8% (for CS+A+ trials) (Fig. 6). (As shown by eq. 3, the overall strength of a connection, given a single modulatory parameter, is the sum of the intrinsic connection strength [A] and the modulatory parameter [B] multiplied with its associated input [u]. In the present case, the asymptotic magnitude of the input function is 0.8 for CS+A+ trials and 0.2 for CS+A− trials [see Fig. 5].)


A dual role for prediction error in associative learning.

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

Learning effects on audio-visual connectivity. Bayesian model comparison showed that the DCM with a single connection from A1 to V1 was superior to the other models. Across subjects, there was a significant “endogenous” or “fixed” strength of the A1 → V1 connection (0.10 s−1, P = 0.003) and a significant learning-induced modulation (magenta arrows) of this connection (P = 0.028). The insets show the parameter estimates for the main effects in both A1 and peripheral V1. The magenta arrows indicate how the main effect in peripheral V1 is modulated by changes in connectivity from A1 to V1 during CS+ trials: over time the response to surprising visual outcomes is upregulated, whereas the response to unsurprising visual outcomes is downregulated. Note that in this plot the magenta arrows designate the direction in which V1 responses change due to modulation of connectivity; for quantitative information on this modulatory effect, see the main text.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Learning effects on audio-visual connectivity. Bayesian model comparison showed that the DCM with a single connection from A1 to V1 was superior to the other models. Across subjects, there was a significant “endogenous” or “fixed” strength of the A1 → V1 connection (0.10 s−1, P = 0.003) and a significant learning-induced modulation (magenta arrows) of this connection (P = 0.028). The insets show the parameter estimates for the main effects in both A1 and peripheral V1. The magenta arrows indicate how the main effect in peripheral V1 is modulated by changes in connectivity from A1 to V1 during CS+ trials: over time the response to surprising visual outcomes is upregulated, whereas the response to unsurprising visual outcomes is downregulated. Note that in this plot the magenta arrows designate the direction in which V1 responses change due to modulation of connectivity; for quantitative information on this modulatory effect, see the main text.
Mentions: Because the learning effect was mainly driven under CS+ blocks, we focused on changes in connectivity between auditory and visual cortices during incidental learning of the predictive attributes of CS+ trials (see Fig. 6). Bayesian model comparison showed that a DCM with a single connection from A1 to V1 (model 1) was superior to alternative models with reciprocal connections (group Bayes factor in favor of model 1: 2.1 × 1017 and 2.2 × 1018 when compared with model 2 and model 3, respectively). Across subjects, the A1 → V1 connection in the optimum model had an average strength of 0.10 s−1 (p = 0.003, df = 13, t = 3.57). During CS+ trials, this connection was significantly modulated by learning, depending on whether the visual stimulus was present or not (i.e., CS+ × (V+ vs. V−) × ϕ in Fig. 6). Note that the modulatory variable in the DCM corresponds to the interaction of the auditory prediction with the visual outcome during CS+ trials. It accounts for a learning-dependent contribution from CS+ responses in auditory cortex to visual cortex responses that depends on whether the visual stimulus was present or not (c.f., a prediction error mediated by top-down signals from auditory areas). Quantitatively, the strength of this modulation was −0.01 s−1 (p = 0.028, df = 13, t = 2.49). This corresponds to learning-induced changes in connectivity ranging from 2% (for CS+A− trials) to 8% (for CS+A+ trials) (Fig. 6). (As shown by eq. 3, the overall strength of a connection, given a single modulatory parameter, is the sum of the intrinsic connection strength [A] and the modulatory parameter [B] multiplied with its associated input [u]. In the present case, the asymptotic magnitude of the input function is 0.8 for CS+A+ trials and 0.2 for CS+A− trials [see Fig. 5].)

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