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New Bayesian hypotheses in predictive processing are challenging to generate. Models either lack brain implementation or fail to explain key cognitive functions, posing a scaling-up problem for Bayesian inference.

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Area of Science:

  • Cognitive Science
  • Neuroscience
  • Computational Neuroscience

Background:

  • Predictive processing offers a unified framework for cognition using Bayesian inference.
  • Hypothesis generation is crucial for Bayesian inference but poses a challenge for predictive processing.
  • Existing models of hypothesis generation fall into two categories: cognitive-level and neurobiological architecture-inspired.

Purpose of the Study:

  • To examine how new Bayesian hypotheses are generated within the predictive processing framework.
  • To analyze the limitations of current cognitive-level and neurobiological models of hypothesis generation.
  • To identify the challenges in scaling up predictive processing to explain complex cognitive abilities.

Main Methods:

  • Review and analysis of cognitive-level models of hypothesis generation.
  • Examination of neurobiological architecture-inspired models of hypothesis generation.
  • Comparative analysis of the strengths and weaknesses of both model types.

Main Results:

  • Cognitive-level models lack clear brain implementation and rely on external assumptions.
  • Neurobiological models, while brain-inspired, struggle to account for categorization and few-shot learning.
  • A significant gap exists between models that are brain-implementable and those that explain broad cognitive capacities.

Conclusions:

  • The predictive processing framework faces a "scaling-up" challenge in generating novel hypotheses.
  • Bridging the gap between cognitive and neurobiological models is essential for advancing predictive processing.
  • Future research must reconcile brain-like mechanisms with the full spectrum of human cognition within a Bayesian framework.