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A predictive processing framework of tool use.

Michiel van Elk1

  • 1Institute of Psychology, Leiden University, Leiden, The Netherlands.

Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
|April 20, 2021
PubMed
Summary
This summary is machine-generated.

Predictive processing offers a unified framework for understanding human tool use and innovation. This theory integrates cognitive, developmental, and evolutionary perspectives for a comprehensive view of tool-related skills.

Keywords:
ApraxiaDevelopmental psychologyEvolutionary psychologyPredictive processingTool use

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Evolutionary Psychology
  • Anthropology

Background:

  • Human tool use and innovation are complex abilities.
  • Existing frameworks may not fully capture the interplay of cognitive and evolutionary factors.
  • Understanding the neurocognitive underpinnings of tool use is crucial.

Purpose of the Study:

  • To introduce predictive processing as a unifying framework for tool use and innovation.
  • To integrate cognitive, developmental, and evolutionary perspectives on tool use.
  • To propose future research directions for testing the predictive processing model of tool use.

Main Methods:

  • Conceptual framework development.
  • Review of infant development, neuropsychological, and neuroscientific literature.
  • Integration of functional-evolutionary perspectives and comparative/cross-cultural studies.
  • Discussion of predictive processing principles (Bayesian inference, prediction-error updating).

Main Results:

  • Predictive processing accounts for tool use development in infants and existing neuroscientific findings.
  • A functional-evolutionary perspective complements predictive processing by linking mechanisms to adaptive functions.
  • Tools can drive cumulative cultural and technological evolution.
  • Predictive processing principles address debates in evolutionary psychology (modularity, exploitation vs. innovation).

Conclusions:

  • Predictive processing provides a robust framework for understanding human tool use and innovation.
  • Integrating cognitive and evolutionary approaches offers deeper insights into tool-related behaviors.
  • Further empirical testing is needed to validate the predictive processing model of tool use.