Synergizing habits and goals with variational Bayes

Dongqi Han (), Kenji Doya, Dongsheng Li and Jun Tani
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Dongqi Han: Microsoft Research Asia
Kenji Doya: Okinawa Institute of Science and Technology
Dongsheng Li: Microsoft Research Asia
Jun Tani: Okinawa Institute of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-14

Abstract: Abstract Behaving efficiently and flexibly is crucial for biological and artificial embodied agents. Behavior is generally classified into two types: habitual (fast but inflexible), and goal-directed (flexible but slow). While these two types of behaviors are typically considered to be managed by two distinct systems in the brain, recent studies have revealed a more sophisticated interplay between them. We introduce a theoretical framework using variational Bayesian theory, incorporating a Bayesian intention variable. Habitual behavior depends on the prior distribution of intention, computed from sensory context without goal-specification. In contrast, goal-directed behavior relies on the goal-conditioned posterior distribution of intention, inferred through variational free energy minimization. Assuming that an agent behaves using a synergized intention, our simulations in vision-based sensorimotor tasks explain the key properties of their interaction as observed in experiments. Our work suggests a fresh perspective on the neural mechanisms of habits and goals, shedding light on future research in decision making.

Date: 2024
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DOI: 10.1038/s41467-024-48577-7

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