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.

Simulating homeostatic, allostatic and goal-directed forms of interoceptive control using Active Inference

Barca L;Maisto D;Pezzulo;
2022

Abstract

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.
2022
Istituto di Scienze e Tecnologie della Cognizione - ISTC
Interoception
Active inference
Predictive coding
Homeostasis
Allostasis
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/447580
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