Challenges to the Modularity Thesis Under the Bayesian Brain Models.

Area of Science:

• Cognitive Science
• Neuroscience
• Computational Neuroscience

Background:

• The modularity assumption is fundamental in cognitive science, positing distinct computational modules.
• Bayesian Brain (BB) models offer a unified framework for perception, cognition, and action based on prediction-error minimization.
• A key debate concerns whether BB models align with or contradict the principle of mental modularity.

Purpose of the Study:

• To critically examine how Bayesian Brain models address the assumption of modularity in cognitive systems.
• To analyze the attentional-mediation argument defending mental modularity in light of BB models' conception of attention.
• To reconcile the classical bottom-up versus top-down information processing dichotomy within the BB framework.

Main Methods:

• Review of theoretical considerations and empirical findings related to BB models and modularity.
• Analysis of the attentional-mediation argument using BB models' novel approach to attention.
• Examination of the modularity of perceptual and motor systems within the BB context.

Main Results:

• BB models present a challenge to strict modularity by integrating cognitive functions under a unified principle.
• The BB conception of attention offers a new perspective on reconciling top-down influences with sensory processing.
• Evidence suggests BB models can accommodate and explain phenomena previously used to argue against modularity.

Conclusions:

• BB models provide a framework that can potentially unify cognitive processes, questioning traditional modular assumptions.
• Reconciling top-down and bottom-up processing within BB models offers insights into cognitive flexibility.
• Further investigation into the modularity of perceptual and motor systems is warranted within the BB paradigm.