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Attention and prediction in human audition: a lesson from cognitive psychophysiology.

Schröger E, Marzecová A, SanMiguel I - Eur. J. Neurosci. (2015)

Bottom Line: Auditory attention typically yields enhanced brain activity, whereas auditory prediction often results in attenuated brain responses.As predictions encode contents and confidence in the sensory data, and as gain can be modulated by the intention of the listener and by the predictability of the input, various possibilities for interactions between attention and prediction can be unfolded.From this perspective, the traditional distinction between bottom-up/exogenous and top-down/endogenous driven attention can be revisited and the classic concepts of attentional gain and attentional trace can be integrated.

View Article: PubMed Central - PubMed

Affiliation: Institute for Psychology, BioCog - Cognitive and Biological Psychology, University of Leipzig, Neumarkt 9-19, D-04109, Leipzig, Germany.

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Attention in the predictive coding framework. The predictive (generative) model makes inferences on the content of and the confidence in the sensory evidence [i.e. the prediction error (PE) from a previous level]. The difference between the input and the content of the prediction is expressed in the PE, which updates the model. Sensory ERPs are understood as the transient expression of the PE. Once the input is explained by the prediction, the perceptual problem is solved and we perceive our interpretation of the world. The relevant content of the inference (attentional template or attentional trace) is what we attend to in a particular experimental situation. Confidence in the attended content is higher than the unattended content. Attention improves the precision of the PE (i.e. it reduces the estimated variability) for the attended content via the predictive model. This results in a modulation of the gain of the PE. Thus, attention will usually increase the amplitude of the PE, which will in turn make the predictive model more accurate. As we usually have several coexisting predictive models, which compete for dominance, attention exerts an indirect bias towards the model that contains the relevant content. The impact of attention on the precision of the PE constitutes an independent factor in the dynamic system of feedback/feedforward recurrent loops, which may interact with other factors of the predictive model (e.g. with the predictability of the input). Thus, the (partly) contradictory effects of attention and prediction reported from ERP research can be explained by the interaction between content prediction and precision estimation.
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fig04: Attention in the predictive coding framework. The predictive (generative) model makes inferences on the content of and the confidence in the sensory evidence [i.e. the prediction error (PE) from a previous level]. The difference between the input and the content of the prediction is expressed in the PE, which updates the model. Sensory ERPs are understood as the transient expression of the PE. Once the input is explained by the prediction, the perceptual problem is solved and we perceive our interpretation of the world. The relevant content of the inference (attentional template or attentional trace) is what we attend to in a particular experimental situation. Confidence in the attended content is higher than the unattended content. Attention improves the precision of the PE (i.e. it reduces the estimated variability) for the attended content via the predictive model. This results in a modulation of the gain of the PE. Thus, attention will usually increase the amplitude of the PE, which will in turn make the predictive model more accurate. As we usually have several coexisting predictive models, which compete for dominance, attention exerts an indirect bias towards the model that contains the relevant content. The impact of attention on the precision of the PE constitutes an independent factor in the dynamic system of feedback/feedforward recurrent loops, which may interact with other factors of the predictive model (e.g. with the predictability of the input). Thus, the (partly) contradictory effects of attention and prediction reported from ERP research can be explained by the interaction between content prediction and precision estimation.

Mentions: We would like to bring the threads presented throughout this article together (Fig.4). The evidence reviewed has clarified three main points: (1) prediction and attention are different mechanisms; (2) prediction and attention have often been confused in previous research; and (3) prediction and attention are interdependent.


Attention and prediction in human audition: a lesson from cognitive psychophysiology.

Schröger E, Marzecová A, SanMiguel I - Eur. J. Neurosci. (2015)

Attention in the predictive coding framework. The predictive (generative) model makes inferences on the content of and the confidence in the sensory evidence [i.e. the prediction error (PE) from a previous level]. The difference between the input and the content of the prediction is expressed in the PE, which updates the model. Sensory ERPs are understood as the transient expression of the PE. Once the input is explained by the prediction, the perceptual problem is solved and we perceive our interpretation of the world. The relevant content of the inference (attentional template or attentional trace) is what we attend to in a particular experimental situation. Confidence in the attended content is higher than the unattended content. Attention improves the precision of the PE (i.e. it reduces the estimated variability) for the attended content via the predictive model. This results in a modulation of the gain of the PE. Thus, attention will usually increase the amplitude of the PE, which will in turn make the predictive model more accurate. As we usually have several coexisting predictive models, which compete for dominance, attention exerts an indirect bias towards the model that contains the relevant content. The impact of attention on the precision of the PE constitutes an independent factor in the dynamic system of feedback/feedforward recurrent loops, which may interact with other factors of the predictive model (e.g. with the predictability of the input). Thus, the (partly) contradictory effects of attention and prediction reported from ERP research can be explained by the interaction between content prediction and precision estimation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4402002&req=5

fig04: Attention in the predictive coding framework. The predictive (generative) model makes inferences on the content of and the confidence in the sensory evidence [i.e. the prediction error (PE) from a previous level]. The difference between the input and the content of the prediction is expressed in the PE, which updates the model. Sensory ERPs are understood as the transient expression of the PE. Once the input is explained by the prediction, the perceptual problem is solved and we perceive our interpretation of the world. The relevant content of the inference (attentional template or attentional trace) is what we attend to in a particular experimental situation. Confidence in the attended content is higher than the unattended content. Attention improves the precision of the PE (i.e. it reduces the estimated variability) for the attended content via the predictive model. This results in a modulation of the gain of the PE. Thus, attention will usually increase the amplitude of the PE, which will in turn make the predictive model more accurate. As we usually have several coexisting predictive models, which compete for dominance, attention exerts an indirect bias towards the model that contains the relevant content. The impact of attention on the precision of the PE constitutes an independent factor in the dynamic system of feedback/feedforward recurrent loops, which may interact with other factors of the predictive model (e.g. with the predictability of the input). Thus, the (partly) contradictory effects of attention and prediction reported from ERP research can be explained by the interaction between content prediction and precision estimation.
Mentions: We would like to bring the threads presented throughout this article together (Fig.4). The evidence reviewed has clarified three main points: (1) prediction and attention are different mechanisms; (2) prediction and attention have often been confused in previous research; and (3) prediction and attention are interdependent.

Bottom Line: Auditory attention typically yields enhanced brain activity, whereas auditory prediction often results in attenuated brain responses.As predictions encode contents and confidence in the sensory data, and as gain can be modulated by the intention of the listener and by the predictability of the input, various possibilities for interactions between attention and prediction can be unfolded.From this perspective, the traditional distinction between bottom-up/exogenous and top-down/endogenous driven attention can be revisited and the classic concepts of attentional gain and attentional trace can be integrated.

View Article: PubMed Central - PubMed

Affiliation: Institute for Psychology, BioCog - Cognitive and Biological Psychology, University of Leipzig, Neumarkt 9-19, D-04109, Leipzig, Germany.

Show MeSH
Related in: MedlinePlus