Sequence-dependent predictive coding during the learning and rewiring of skills

In the constantly changing environment that characterizes our daily lives, the ability to predict and adapt to new circumstances is crucial. This study examines the influence of sequence and knowledge adaptiveness on predictive coding in skill learning and rewiring. Participants were exposed to two different visuomotor sequences with overlapping probabilities. By applying temporal decomposition and multivariate pattern analysis, we dissected the neural underpinnings across different levels of signal coding. The study provides neurophysiological evidence for the influence of knowledge adaptiveness on shaping predictive coding, revealing that these are intricately linked and predominantly manifest at the abstract and motor coding levels. These findings challenge the traditional view of a competitive relationship between learning context and knowledge, suggesting instead a hierarchical integration where their properties are processed simultaneously. This integration facilitates the adaptive reuse of existing knowledge in the face of new learning. By shedding light on the mechanisms of predictive coding in visuomotor sequences, this research contributes to a deeper understanding of how the brain navigates and adapts to environmental changes, offering insights into the foundational processes that underlie learning and adaptation in dynamic contexts.

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Publication Details

Journal
Cerebral Cortex
Published
2025-02-01
DOI
https://doi.org/10.1093/cercor/bhaf025
Citations
3
Primary Topic
Motor Control and Adaptation
Type
article
Field-Weighted Citation Impact
4.29

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article

Sequence-dependent predictive coding during the learning and rewiring of skills

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3 citations
Cerebral Cortex
Motor Control and Adaptation
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article

Sequence-dependent predictive coding during the learning and rewiring of skills

Felipe Pedraza, Dezső Németh, Teodóra Vékony, Ádám Takács, Barbara Tillmann, Christian Beste, Frédéric Haesebaert
article en
3 citations

Abstract

In the constantly changing environment that characterizes our daily lives, the ability to predict and adapt to new circumstances is crucial. This study examines the influence of sequence and knowledge adaptiveness on predictive coding in skill learning and rewiring. Participants were exposed to two different visuomotor sequences with overlapping probabilities. By applying temporal decomposition and multivariate pattern analysis, we dissected the neural underpinnings across different levels of signal coding. The study provides neurophysiological evidence for the influence of knowledge adaptiveness on shaping predictive coding, revealing that these are intricately linked and predominantly manifest at the abstract and motor coding levels. These findings challenge the traditional view of a competitive relationship between learning context and knowledge, suggesting instead a hierarchical integration where their properties are processed simultaneously. This integration facilitates the adaptive reuse of existing knowledge in the face of new learning. By shedding light on the mechanisms of predictive coding in visuomotor sequences, this research contributes to a deeper understanding of how the brain navigates and adapts to environmental changes, offering insights into the foundational processes that underlie learning and adaptation in dynamic contexts.

Cerebral CortexVol. 35(2)
Université Claude Bernard Lyon 1 (FR), Eötvös Loránd University (HU), Universidad de Las Palmas de Gran Canaria (ES), Centre National de la Recherche Scientifique (FR), Inserm (FR), Centre de Recherche en Neurosciences de Lyon (FR), Institute of Cognitive Neuroscience and Psychology (HU), Czech Academy of Sciences, Institute of Psychology (CZ), Laboratoire d'étude de l'apprentissage et du développement (FR), Cognitive Research (United States) (US), Technische Universität Dresden (DE), HUN-REN Research Centre for Natural Sciences (HU)
Deutsche Forschungsgemeinschaft, Agence Nationale de la Recherche, Institut National de la Santé et de la Recherche Médicale, Magyar Tudományos Akadémia, Université de Lyon
Quality Education
Openalex Percentile: Top 7%
Motor Control and Adaptation
4.29
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