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Emotor control: computations underlying bodily resource allocation, emotions, and confidence.

Adam Kepecs1, Brett D Mensh2

  • 1Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, USA.

Dialogues in Clinical Neuroscience
|February 13, 2016
PubMed
Summary

Emotions are proposed as "emotor" control systems, reallocating bodily resources via neural computations. This framework, exemplified by decision confidence, offers insights into brain function and psychiatric disorders.

Keywords:
RDoCResearch Domain Criteriacomputational psychiatrydecision confidencedecision makingmodel-based neuroscience

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Systems Neuroscience

Background:

  • Emotional processes are central to behavior but challenging to study due to their subjective nature.
  • Current psychiatric diagnosis struggles with subjective emotional experiences.
  • Systems neuroscience views behavior as computations instantiated in the brain.

Purpose of the Study:

  • To explore the link between subjective feelings and brain circuits using a computational approach.
  • To propose emotions as a neural computation system for resource reallocation (emotor control).
  • To bridge the gap between computational models of emotion and subjective experience.

Main Methods:

  • Applying systems neuroscience insights to the study of emotions.
  • Developing a computational framework for emotions as "emotor" control.
  • Reviewing research on "confidence" as a case study.

Main Results:

  • Emotions are hypothesized as neural computations reallocating bodily resources via smooth muscles.
  • Decision confidence, both subjective and statistical, has a revealed neural basis.
  • This model provides a bridge between computational and subjective aspects of confidence.

Conclusions:

  • The "emotor" control framework offers a new perspective on emotions.
  • Disorders in confidence computations may underlie psychopathologies.
  • A computational approach to emotions aligns with emerging psychiatric nosology models.