Author: Alexander Tschantz1, Laura Barca2, Domenico Maisto3, Christopher L Buckley4, Anil K Seth5, Giovanni Pezzulo6
Affiliation: <sup>1</sup> Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton, United Kingdom and Department of Informatics, University of Sussex, Brighton, United Kingdom.
<sup>2</sup> Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, Rome 00185, Italy.
<sup>3</sup> Institute for High Performance Computing and Networking, National Research Council, Via P. Castellino, 111, Naples 80131, Italy.
<sup>4</sup> EASY Group-Sussex Neuroscience, Department of Informatics, University of Sussex, Brighton, United Kingdom.
<sup>5</sup> Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton, United Kingdom and Department of Informatics, University of Sussex, Brighton, United Kingdom; Canadian Institute for Advanced Research (CIFAR): Program on Brain, Mind, and Consciousness, Toronto, Ontario. Canada.
<sup>6</sup> Institute of Cognitive Sciences and Technologies, National Research Council, Via San Martino della Battaglia 44, Rome 00185, Italy. Electronic address: giovanni.pezzulo@istc.cnr.it.
Conference/Journal: Biol Psychol
Date published: 2022 Jan 17
Other:
Special Notes: doi: 10.1016/j.biopsycho.2022.108266. , Word Count: 158
The adaptive regulation of bodily and interoceptive parameters, such as body temperature, thirst and hunger is a central problem for any biological organism. Here, we present a series of simulations using the framework of active inference to formally characterize interoceptive control and some of its dysfunctions. We start from the premise that the goal of interoceptive control is to minimize a discrepancy between expected and actual interoceptive sensations (i.e., a prediction error or free energy). Importantly, living organisms can achieve this goal by using various forms of interoceptive control: homeostatic, allostatic and goal-directed. We provide a computationally-guided analysis of these different forms of interoceptive control, by showing that they correspond to distinct generative models within Active inference. We discuss how these generative models can support empirical research through enabling fine-grained predictions about physiological and brain signals that may accompany both adaptive and maladaptive interoceptive control.
Keywords: Active Inference; Allostasis; Homeostasis; Interoception; Predictive Coding.
PMID: 35051559 DOI: 10.1016/j.biopsycho.2022.108266