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In the Body’s Eye: The Computational Anatomy of Interoceptive Inference

Micah Allen, Andrew Levy, Thomas Parr, Karl J. Friston

bioRxiv Preprint Server April 10, 2019 preprint DOI: 10.1101/603928 via bioRxiv

Summary

A formal model of cardiac active inference shows how heart signals influence perception of the outside world and confidence in that perception. Simulated experiments reproduce the defensive startle reflex and links between the cardiac cycle and fear perception. Simulated interoceptive lesions blunt fear expectations, cause psychosomatic hallucinations, and worsen metacognitive biases. Synthetic heart-rate variability analyses reveal how arousal-priors and visceral prediction errors create individual patterns of physiological reactivity. The model offers a way to computationally characterize disordered brain-body interaction.

Study at a glance

Design theoretical or philosophical paper
Key finding A formal model of cardiac active inference explains how ascending cardiac signals entrain exteroceptive sensory perception and confidence, reproducing the defensive startle reflex and effects linking the cardiac cycle to fear perception.

Abstract

A growing body of evidence highlights the intricate linkage of exteroceptive perception to the rhythmic activity of the visceral body. In parallel, interoceptive inference theories of emotion and self-consciousness are on the rise in cognitive science. However, thus far no formal theory has emerged to integrate these twin domains; instead most extant work is conceptual in nature. Here, we introduce a formal model of cardiac active inference, which explains how ascending cardiac signals entrain exteroceptive sensory perception and confidence. Through simulated psychophysics, we reproduce the defensive startle reflex and commonly reported effects linking the cardiac cycle to fear perception. We further show that simulated ‘interoceptive lesions’ blunt fear expectations, induce psychosomatic hallucinations, and exacerbate metacognitive biases. Through synthetic heart-rate variability analyses, we illustrate how the balance of arousal-priors and visceral prediction errors produces idiosyncratic patterns of physiological reactivity. Our model thus offers the possibility to computationally phenotype disordered brain-body interaction.

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