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Evaluating the neurophysiological evidence for predictive processing as a model of perception.

Kevin S Walsh1, David P McGovern1,2, Andy Clark3,4

  • 1Trinity College Institute of Neuroscience and School of Psychology, Trinity College Dublin, Dublin, Ireland.

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Summary

Hierarchical feedforward models dominated perception research, but predictive processing (PP) models, using prediction and error neurons, are gaining traction. New neurophysiological research is evaluating the empirical support for PP models.

Keywords:
neurophysiologyperceptionperceptual inferencepredictive codingpredictive processing

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Area of Science:

  • Cognitive Neuroscience
  • Neuroscience
  • Perception Research

Background:

  • Hierarchical feedforward models have long dominated theories of perceptual experience.
  • These models propose sensory input processing through successive feature detectors.
  • Predictive processing (PP) models offer an alternative, viewing perception as an inferential process.

Purpose of the Study:

  • To review recent neurophysiological research on predictive processing (PP) models.
  • To evaluate the empirical evidence supporting the core claims of PP.
  • To address the methodological challenges in testing PP model predictions.

Main Methods:

  • Review of recent human and nonhuman neurophysiological studies.
  • Analysis of research addressing the unique predictions of PP models.
  • Synthesis of findings to assess the evidential basis for PP.

Main Results:

  • A surge in research has emerged due to technological and theoretical advancements.
  • This research aims to fill the empirical gap in support of PP models.
  • The review will critically assess the extent to which findings support PP claims.

Conclusions:

  • Predictive processing models are increasingly influential in cognitive neuroscience.
  • PP models are criticized for a lack of robust empirical support.
  • New research is actively seeking to validate the predictive processing framework.