Research Topic

Predictive Mechanisms in Action, Perception, Cognition, and Clinical Disorders

About this Research Topic

Our perception of the world is highly influenced by our prior knowledge. In particular, prior knowledge is useful for the formation of predictions, which constrain sensory experience, making perception more efficient. Mismatches between predictions and incoming information result in prediction errors, which allow us to revise our predictions and react accordingly.

Several influential theories posit that prediction may be a fundamental mechanism of brain function. Over the last decade, predictive frameworks of brain function have increasingly gained popularity to explain action, perception, cognition, and even clinical disorders. Within each of these fields, researchers have used a variety of techniques (from single-cell recordings to psychophysics and human neuroimaging) and experimental paradigms (from repetition suppression to statistical learning) to try to elucidate predictive mechanisms.

Though often studied as a domain-general mechanism, it may be the case that prediction is actually domain-specific and differs as a product of modality. In fact, recent research suggests that predictive processes may be deployed differently across domains of action, perception, cognition, and psychiatry. Although significant progress has been made in the study of predictive mechanisms within each of these domains, we have not yet begun to understand the parallels between them nor identify the key differences.

This Research Topic aims to explore guiding principles, theoretical frameworks, computational accounts, and empirical research on predictive mechanisms across domains and levels of processing. A thorough exploration of predictive mechanisms within each field may help us gain a more comprehensive understanding of the limits, constraints, and generalizability of these mechanisms across fields.

We welcome articles focusing on:

- Psychophysical, behavioral, or neuroimaging studies on predictive mechanisms in the domains of action, perception, cognition, and/or clinical disorders.
- Theoretical accounts of predictive mechanisms within or across domains.
- Computational approaches to the investigation of predictive processes in humans.
- Articles focusing on Bayesian approaches, statistical learning, active inference, belief updating, etc. that incorporate predictive language.
- Evidence of predictive challenges in clinical disorders.
- Novel experimental paradigms to elucidate predictive mechanisms in humans.


Keywords: Prediction, Perception, Action, Cognition, Psychiatry


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Our perception of the world is highly influenced by our prior knowledge. In particular, prior knowledge is useful for the formation of predictions, which constrain sensory experience, making perception more efficient. Mismatches between predictions and incoming information result in prediction errors, which allow us to revise our predictions and react accordingly.

Several influential theories posit that prediction may be a fundamental mechanism of brain function. Over the last decade, predictive frameworks of brain function have increasingly gained popularity to explain action, perception, cognition, and even clinical disorders. Within each of these fields, researchers have used a variety of techniques (from single-cell recordings to psychophysics and human neuroimaging) and experimental paradigms (from repetition suppression to statistical learning) to try to elucidate predictive mechanisms.

Though often studied as a domain-general mechanism, it may be the case that prediction is actually domain-specific and differs as a product of modality. In fact, recent research suggests that predictive processes may be deployed differently across domains of action, perception, cognition, and psychiatry. Although significant progress has been made in the study of predictive mechanisms within each of these domains, we have not yet begun to understand the parallels between them nor identify the key differences.

This Research Topic aims to explore guiding principles, theoretical frameworks, computational accounts, and empirical research on predictive mechanisms across domains and levels of processing. A thorough exploration of predictive mechanisms within each field may help us gain a more comprehensive understanding of the limits, constraints, and generalizability of these mechanisms across fields.

We welcome articles focusing on:

- Psychophysical, behavioral, or neuroimaging studies on predictive mechanisms in the domains of action, perception, cognition, and/or clinical disorders.
- Theoretical accounts of predictive mechanisms within or across domains.
- Computational approaches to the investigation of predictive processes in humans.
- Articles focusing on Bayesian approaches, statistical learning, active inference, belief updating, etc. that incorporate predictive language.
- Evidence of predictive challenges in clinical disorders.
- Novel experimental paradigms to elucidate predictive mechanisms in humans.


Keywords: Prediction, Perception, Action, Cognition, Psychiatry


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

01 August 2021 Abstract
30 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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Topic Editors

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Submission Deadlines

01 August 2021 Abstract
30 November 2021 Manuscript

Participating Journals

Manuscripts can be submitted to this Research Topic via the following journals:

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