Limits...
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.

Show MeSH

Related in: MedlinePlus

Skeleton of the flow of information as postulated by the predictive coding theory. Generative models encoding the inferred causes of the sensorial input in representational units (R), located at a higher level, send predictions down to a lower level, where they are compared with the input arriving at the lower level from a still lower level (hierarchical system). The mismatch between the two is computed by prediction error (PE) units and sent forward to the higher level, so that the generative model can be improved and kept faithful. As only the PE is passed on to the higher levels, the amount of sensory data that need to be processed further is reduced to only those parts that are not already accounted for by the model. The system tries to minimize the PE, which is assumed to be generated by superficial pyramidal cells, where ERPs (and high-frequency oscillatory activity) are generated to a large extent. Deep pyramidal cells seem to be involved in the transmission of information backwards throughout the hierarchy, where more sustained ERPs (and low-frequency oscillatory activity) are generated. This is a simplified version of a figure published in Friston (2005) as Fig. 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4402002&req=5

fig01: Skeleton of the flow of information as postulated by the predictive coding theory. Generative models encoding the inferred causes of the sensorial input in representational units (R), located at a higher level, send predictions down to a lower level, where they are compared with the input arriving at the lower level from a still lower level (hierarchical system). The mismatch between the two is computed by prediction error (PE) units and sent forward to the higher level, so that the generative model can be improved and kept faithful. As only the PE is passed on to the higher levels, the amount of sensory data that need to be processed further is reduced to only those parts that are not already accounted for by the model. The system tries to minimize the PE, which is assumed to be generated by superficial pyramidal cells, where ERPs (and high-frequency oscillatory activity) are generated to a large extent. Deep pyramidal cells seem to be involved in the transmission of information backwards throughout the hierarchy, where more sustained ERPs (and low-frequency oscillatory activity) are generated. This is a simplified version of a figure published in Friston (2005) as Fig. 2.

Mentions: The most influential computational implementation of these principles is Friston's predictive coding theory (e.g. Friston, 2009; Friston & Kiebel, 2009). This model proposes that the hierarchical message passing takes place between representational units that encode the inferred causes of sensory input (the model of predicted states) and prediction error units. Prediction error units compare predictions received from the higher level via backward, top-down projections and inputs received from the lower level via feedforward, bottom-up projections. This dynamic system of feedback/feedforward recurrent loops aims at minimizing the prediction error. At each level of the cortical hierarchy, only the prediction error is passed onto the higher levels (Fig.1). As a consequence, the amount of sensory data that are fed forward and need to be processed further is reduced to only those parts that are not already accounted for by the model. In fact, the predictive coding theory interprets electrophysiological measures of brain activity as an expression mainly of prediction error (see, e.g. Garrido et al., 2007, 2009; Feldman & Friston, 2010). From this perspective, sensory ERPs are understood as the transient expression of prediction error (Friston, 2005), which is going to be suppressed by increasingly improved predictions from higher areas. Once the input is explained by the prediction, the perceptual problem is solved and we perceive our internal model, i.e. the content that is specified by representational neurons. This solution of the perceptual problem is more difficult for novel stimuli and for stimuli that do not fit to the context in which they appear. At first glance, it may sound counter-intuitive to cognitive psychophysiologists that ERPs such as the N100 can be regarded as prediction error signals. However, it provides a coherent interpretation of many experimental results from prediction research, which we will address in this section, from attention research (Section Cognitive psychophysiology of auditory attention) and from research tapping into the interaction between prediction and attention (Section Studies on the relation between auditory attention and prediction).


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

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

Skeleton of the flow of information as postulated by the predictive coding theory. Generative models encoding the inferred causes of the sensorial input in representational units (R), located at a higher level, send predictions down to a lower level, where they are compared with the input arriving at the lower level from a still lower level (hierarchical system). The mismatch between the two is computed by prediction error (PE) units and sent forward to the higher level, so that the generative model can be improved and kept faithful. As only the PE is passed on to the higher levels, the amount of sensory data that need to be processed further is reduced to only those parts that are not already accounted for by the model. The system tries to minimize the PE, which is assumed to be generated by superficial pyramidal cells, where ERPs (and high-frequency oscillatory activity) are generated to a large extent. Deep pyramidal cells seem to be involved in the transmission of information backwards throughout the hierarchy, where more sustained ERPs (and low-frequency oscillatory activity) are generated. This is a simplified version of a figure published in Friston (2005) as Fig. 2.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig01: Skeleton of the flow of information as postulated by the predictive coding theory. Generative models encoding the inferred causes of the sensorial input in representational units (R), located at a higher level, send predictions down to a lower level, where they are compared with the input arriving at the lower level from a still lower level (hierarchical system). The mismatch between the two is computed by prediction error (PE) units and sent forward to the higher level, so that the generative model can be improved and kept faithful. As only the PE is passed on to the higher levels, the amount of sensory data that need to be processed further is reduced to only those parts that are not already accounted for by the model. The system tries to minimize the PE, which is assumed to be generated by superficial pyramidal cells, where ERPs (and high-frequency oscillatory activity) are generated to a large extent. Deep pyramidal cells seem to be involved in the transmission of information backwards throughout the hierarchy, where more sustained ERPs (and low-frequency oscillatory activity) are generated. This is a simplified version of a figure published in Friston (2005) as Fig. 2.
Mentions: The most influential computational implementation of these principles is Friston's predictive coding theory (e.g. Friston, 2009; Friston & Kiebel, 2009). This model proposes that the hierarchical message passing takes place between representational units that encode the inferred causes of sensory input (the model of predicted states) and prediction error units. Prediction error units compare predictions received from the higher level via backward, top-down projections and inputs received from the lower level via feedforward, bottom-up projections. This dynamic system of feedback/feedforward recurrent loops aims at minimizing the prediction error. At each level of the cortical hierarchy, only the prediction error is passed onto the higher levels (Fig.1). As a consequence, the amount of sensory data that are fed forward and need to be processed further is reduced to only those parts that are not already accounted for by the model. In fact, the predictive coding theory interprets electrophysiological measures of brain activity as an expression mainly of prediction error (see, e.g. Garrido et al., 2007, 2009; Feldman & Friston, 2010). From this perspective, sensory ERPs are understood as the transient expression of prediction error (Friston, 2005), which is going to be suppressed by increasingly improved predictions from higher areas. Once the input is explained by the prediction, the perceptual problem is solved and we perceive our internal model, i.e. the content that is specified by representational neurons. This solution of the perceptual problem is more difficult for novel stimuli and for stimuli that do not fit to the context in which they appear. At first glance, it may sound counter-intuitive to cognitive psychophysiologists that ERPs such as the N100 can be regarded as prediction error signals. However, it provides a coherent interpretation of many experimental results from prediction research, which we will address in this section, from attention research (Section Cognitive psychophysiology of auditory attention) and from research tapping into the interaction between prediction and attention (Section Studies on the relation between auditory attention and prediction).

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