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Processing Fluency and Predictive Processing: How the Predictive Mind Becomes Aware of its Cognitive Limitations.

Philippe Servajean1,2, Wanja Wiese3

  • 1Laboratory of Psychology and NeuroCognition, University of Grenoble Alpes.

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|November 25, 2024
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Summary

This study explores how predictive processing and processing fluency work together. It explains how the brain infers the need for structural changes in its models to learn environmental regularities.

Keywords:
Bayesian parameter learningMental actionPrecisionPredictive processingProcessing fluencyRelative fluencySelective attentionStructure learning

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Neuroscience

Background:

  • Predictive processing is a key framework for cognition, often focusing on parameter learning in generative models.
  • Comprehensive learning accounts require understanding how the brain learns model structure, termed structure learning.
  • Structure learning implies actual modifications to the brain's generative models.

Purpose of the Study:

  • To elucidate the processes preceding structural changes in generative models during learning.
  • To demonstrate the compatibility between predictive processing and processing fluency theory.
  • To explain how the brain infers the necessity for structural model updates.

Main Methods:

  • Integrating predictive processing with established constructs of processing fluency.
  • Utilizing the predictive processing framework to model the inference of structural learning needs.
  • Examining the upstream processes that may trigger structural changes.

Main Results:

  • Predictive processing comprehensively accounts for key concepts in processing fluency.
  • A novel framework is proposed where processing fluency guides inferences about structural learning.
  • The brain's inference of structural needs is linked to processing fluency.

Conclusions:

  • Predictive processing and processing fluency are highly compatible and can be unified.
  • Processing fluency serves as a crucial signal for inferring the need for structure learning.
  • This inference mechanism provides a pathway for triggering necessary structural changes in generative models.