Embodied decisions as active inference

Decision-making is often conceptualized as a serial process, during which sensory evidence is accumulated for the choice alternatives until a certain threshold is reached, at which point a decision is made and an action is executed. This decide-then-act perspective has successfully explained various facets of perceptual and economic decisions in the laboratory, in which action dynamics are usually irrelevant to the choice. However, living organisms often face another class of decisions-called embodied decisions-that require selecting between potential courses of actions to be executed timely in a dynamic environment, e.g., for a lion, deciding which gazelle to chase and how fast to do so. Studies of embodied decisions reveal two aspects of goal-directed behavior in stark contrast to the serial view. First, that decision and action processes can unfold in parallel; second, that action-related components, such as the motor costs associated with selecting a particular choice alternative or required to "change mind" between choice alternatives, exert a feedback effect on the decision taken. Here, we show that these signatures of embodied decisions emerge naturally in active inference-a framework that simultaneously optimizes perception and action, according to the same (free energy minimization) imperative. We show that optimizing embodied choices requires a continuous feedback loop between motor planning (where beliefs about choice alternatives guide action dynamics) and motor inference (where action dynamics finesse beliefs about choice alternatives). Furthermore, our active inference simulations reveal the normative character of embodied decisions in ecological settings - namely, achieving an effective balance between a high accuracy and a low risk of missing valid opportunities.

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Publication Details

Journal
PLoS Computational Biology
Published
2025-06-18
DOI
https://doi.org/10.1371/journal.pcbi.1013180
Citations
4
Primary Topic
Embodied and Extended Cognition
Type
article
Field-Weighted Citation Impact
5.72

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article

Embodied decisions as active inference

Ivilin Peev Stoianov, Matteo Priorelli, Giovanni Pezzulo
4 citations
PLoS Computational Biology
Embodied and Extended Cognition
5.72
article

Embodied decisions as active inference

Ivilin Peev Stoianov, Matteo Priorelli, Giovanni Pezzulo
article en
4 citations

Abstract

Decision-making is often conceptualized as a serial process, during which sensory evidence is accumulated for the choice alternatives until a certain threshold is reached, at which point a decision is made and an action is executed. This decide-then-act perspective has successfully explained various facets of perceptual and economic decisions in the laboratory, in which action dynamics are usually irrelevant to the choice. However, living organisms often face another class of decisions-called embodied decisions-that require selecting between potential courses of actions to be executed timely in a dynamic environment, e.g., for a lion, deciding which gazelle to chase and how fast to do so. Studies of embodied decisions reveal two aspects of goal-directed behavior in stark contrast to the serial view. First, that decision and action processes can unfold in parallel; second, that action-related components, such as the motor costs associated with selecting a particular choice alternative or required to "change mind" between choice alternatives, exert a feedback effect on the decision taken. Here, we show that these signatures of embodied decisions emerge naturally in active inference-a framework that simultaneously optimizes perception and action, according to the same (free energy minimization) imperative. We show that optimizing embodied choices requires a continuous feedback loop between motor planning (where beliefs about choice alternatives guide action dynamics) and motor inference (where action dynamics finesse beliefs about choice alternatives). Furthermore, our active inference simulations reveal the normative character of embodied decisions in ecological settings - namely, achieving an effective balance between a high accuracy and a low risk of missing valid opportunities.

PLoS Computational BiologyVol. 21(6)
National Research Council (IT), Institute of Cognitive Sciences and Technologies (IT), Sapienza University of Rome (IT)
Ministero dell'Università e della Ricerca, Horizon 2020 Framework Programme, H2020 European Research Council
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Embodied and Extended Cognition
5.72
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