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A challenge for predictive coding: Representational or experiential diversity?

Martina G Vilas1, Lucia Melloni1,2

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Predictive processing (PP) needs to explain diverse brain representations. This study argues PP fails at the experiential level, challenging its unifying theory status.

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Area of Science:

  • Neuroscience
  • Cognitive Science
  • Computational Neuroscience

Background:

  • Predictive processing (PP) is a prominent framework for understanding brain function.
  • A key challenge for PP is explaining the diversity of neural representations.
  • Gilead et al. proposed that this diversity necessitates a multi-process theory, potentially beyond PP's scope.

Purpose of the Study:

  • To evaluate the capacity of predictive processing (PP) to account for representational diversity in brain function.
  • To challenge the claim that representational diversity inherently requires a multi-process theory beyond PP.
  • To identify specific limitations of PP concerning the experiential level of representations.

Main Methods:

  • Conceptual analysis of predictive processing principles.
  • Critique of existing arguments regarding representational diversity and PP.
  • Theoretical proposal for PP's shortcomings at the experiential level.

Main Results:

  • Predictive processing (PP) faces challenges in fully accommodating the richness of brain representations.
  • The argument that representational diversity necessitates a multi-process theory is contested.
  • PP's current formulation is insufficient to explain the experiential level of representations.

Conclusions:

  • Predictive processing (PP) requires refinement to adequately address representational diversity.
  • The experiential level of representation presents a significant hurdle for PP as a unifying theory of brain function.
  • Further theoretical development is needed to integrate subjective experience within predictive processing frameworks.