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  3. Fixed And Flexible Perceptual Rhythms.
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Fixed and flexible perceptual rhythms.

Aaron Kaltenmaier1, Matthew H Davis2, Clare Press1

  • 1Department of Experimental Psychology and Functional Imaging Laboratory, University College London, London, WC1H 0AP, UK.

Trends in Cognitive Sciences
|June 18, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

Neural oscillations flexibly adapt to external sensory rhythms, challenging fixed-process theories. This study proposes a Bayesian learning model to explain how rhythmic sensory and neural processes combine for perception and action.

Keywords:
entrainmentnatural frequenciesoscillationsperceptionpredictive processingstatistical learning

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Psychology

Background:

  • Sensory input possesses inherent temporal structure, influencing perception.
  • The interaction between sensory rhythms and neural oscillations is a key area of cognitive science research.
  • Existing theories on neural oscillation flexibility (adaptive vs. fixed intrinsic processes) appear contradictory.

Purpose of the Study:

  • To reconcile conflicting theories on neural oscillation dynamics.
  • To investigate how external sensory rhythms and internal neural rhythms are integrated.
  • To propose a unified model for sensory and motor processing based on rhythmic interactions.

Main Methods:

  • Literature review highlighting the dichotomy in current research.
  • Conceptual model development based on statistical (Bayesian) learning principles.
  • Theoretical framework for integrating rhythmic sensory and neural information.

    • Main Results:

    • Identified a critical need to address the combination of rhythmic sources in perception.
    • Proposed a Bayesian learning framework as a unifying principle.
    • Suggested that neural oscillations may dynamically integrate external and internal rhythmic information.

    Conclusions:

    • Neural oscillations are not solely fixed or flexible but dynamically integrated.
    • A Bayesian approach offers a powerful framework for understanding rhythmic interactions in cognition.
    • This work provides a new perspective on how the brain processes temporal information for perception and action.