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Updated: May 10, 2026
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Measuring Attention and Visual Processing Speed by Model-based Analysis of Temporal-order Judgments
Published on: January 23, 2017
Expectation and attention in hierarchical auditory prediction
Srivas Chennu1, Valdas Noreika, David Gueorguiev
1Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0QQ, United Kingdom. sc672@cam.ac.uk
Summary
Attention and expectation in auditory perception are explained by hierarchical predictive coding. Findings show distinct neural processing levels, with early stages influenced by stimulus predictability and later stages by expectation and attention.
Area of Science:
- Neuroscience
- Cognitive Science
- Auditory Perception
Background:
- Hierarchical predictive coding theories propose attention arises from enhanced probabilistic inference precision.
- Expectation biases attention towards contextually anticipated stimuli.
- Electrophysiological signatures like MMN, P300, and CNV are commonly observed in neurodynamics.
Purpose of the Study:
- To investigate the roles of top-down expectation and attentional precision in auditory perception.
- To test how these factors interact with bottom-up stimulus predictability.
- To interpret electrophysiological signatures within a hierarchical predictive coding framework.
Main Methods:
- Independent manipulation of top-down expectation and attentional precision.
- Inclusion of bottom-up stimulus predictability as a factor.
- Analysis of electrophysiological data including mismatch negativity (MMN), P300, and contingent negative variation (CNV).
- Source reconstruction of high-density EEG and intracranial recordings.
Main Results:
- Early processing (MMN) sensitive to predictability; attentional precision enhanced responses, while expectation diminished them.
- Later processing (P300) sensitive to predictability, contingent on attention, and sharpened by expectation.
- Contingent negative variation (CNV) drift served as a marker of top-down expectation.
- Temporal and frontal regions implicated in early and late processing stages, respectively.
Conclusions:
- Findings support an integrative interpretation of MMN, P300, and CNV as reflecting successive levels of predictive complexity.
- Results provide empirical support for predictive coding accounts of attention and expectation.
- The CNV's cortical generators suggest a role in consolidating salient stimuli into conscious perception.
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