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Perspectives on Neuroscience
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What is mood? A computational perspective.

James E Clark1, Stuart Watson1, Karl J Friston2

  • 1Newcastle University,Newcastle Upon Tyne,UK.

Psychological Medicine
|February 27, 2018
PubMed
Summary
This summary is machine-generated.

This review proposes that mood disorders arise from aberrant computational processes in the brain, specifically related to how the brain predicts sensory information and uncertainty.

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Affective Science

Background:

  • Understanding the neurobiology of mood and mood disorders is challenging.
  • Existing research implicates various neuromodulator systems.
  • Current theories lack a unifying framework.

Purpose of the Study:

  • To propose a novel computational framework for understanding mood and mood disorders.
  • To extend the free energy principle and Bayesian brain theories to mood regulation.
  • To explain mood disorders through a computational lens focusing on uncertainty.

Main Methods:

  • Review of existing theories on functional brain organization.
  • Application of the free energy principle (active inference) to mood.
  • Extension of hierarchical Bayesian models to interoceptive and proprioceptive domains.

Main Results:

  • Mood states can be formulated as hierarchical priors over uncertainty (emotions).
  • Aberrant precision weighting of sensory predictions may underlie mood disorders.
  • This framework reconciles research across multiple levels of inquiry.

Conclusions:

  • The free energy principle offers a parsimonious explanation for mood.
  • Mood disorders may stem from computational pathologies in processing interoceptive and proprioceptive information.
  • This approach provides a unified computational account of mood regulation and dysfunction.