Breaking boundaries: the Bayesian Brain Hypothesis for perception and prediction

Inês Hipólito; Michael Kirchhoff

doi:10.1016/j.concog.2023.103510

Breaking boundaries: the Bayesian Brain Hypothesis for perception and prediction

Inês Hipólito^*, Michael Kirchhoff

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

Abstract

This special issue aims to provide a comprehensive overview of the current state of the Bayesian Brain Hypothesis and its standing across neuroscience, cognitive science and the philosophy of cognitive science. By gathering cutting-edge research from leading experts, this issue seeks to showcase the latest advancements in our understanding of the Bayesian brain, as well as its potential implications for future research in perception, cognition, and motor control. A special focus to achieve this aim is adopted in this special issue, as it seeks to explore the relation between two seemingly incompatible frameworks for the understanding of cognitive structure and function: the Bayesian Brain Hypothesis and the Modularity Theory of the Mind. In assessing the compatibility between these theories, the contributors to this special issue open up new pathways of thinking and advance our understanding of cognitive processes.

Original language	English
Article number	103510
Pages (from-to)	1-5
Number of pages	5
Journal	Consciousness and cognition
Volume	111
DOIs	https://doi.org/10.1016/j.concog.2023.103510
Publication status	Published - May 2023

Keywords

Bayesian Brain Hypothesis
Modularity of the Mind
Cognitive processes
Informational boundaries

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10.1016/j.concog.2023.103510

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Breaking boundaries: the Bayesian Brain Hypothesis for perception and prediction

Abstract

Keywords

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