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Control of Eating Behavior Using a Novel Feedback System
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Keep your interoceptive streams under control: An active inference perspective on anorexia nervosa.

Laura Barca1, Giovanni Pezzulo2

  • 1Institute of Cognitive Science and Technologies, ISTC-CNR, Via San Martino della Battaglia, 44, 00141, Rome, Italy. laura.barca@istc.cnr.it.

Cognitive, Affective & Behavioral Neuroscience
|February 9, 2020
PubMed
Summary

Anorexia nervosa (AN) may involve food restriction as a strategy to manage high levels of interoceptive uncertainty. This approach helps minimize internal sensory noise, promoting a stable sense of self.

Keywords:
Active inferenceAnorexia nervosaCognitive controlInteroceptionSelf-coherenceStarvationUncertainty

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Internal Medicine

Background:

  • Anorexia nervosa (AN) is defined by energy intake restriction leading to significantly low body weight.
  • Recent theories view the brain as a predictive machine processing internal and external signals.
  • Interoceptive (active) inference involves the brain inferring and controlling bodily signals.

Purpose of the Study:

  • To propose a novel interpretation of food restriction in AN.
  • To explore the role of interoceptive uncertainty in AN.
  • To link eating restrictions to the management of bodily signal inference.

Main Methods:

  • Theoretical interpretation based on predictive processing models.
  • Elaboration on active inference principles applied to AN.
  • Conceptual analysis of interoceptive streams and self-regulation.

Main Results:

  • Food restriction in AN may primarily aim to control excessive interoceptive uncertainty.
  • Eating restrictions can be an active strategy to reduce internal sensory noise.
  • This strategy may amplify autonomic hunger signals to stabilize the interoceptive self.

Conclusions:

  • Interoceptive uncertainty management offers a new perspective on AN's core mechanisms.
  • Food restriction in AN could be a maladaptive attempt to achieve interoceptive stability.
  • Understanding AN through active inference may inform novel therapeutic targets.