An integrative computational approach for obstacle avoidance during action selection

Abstract excerpt

Action selection in cluttered environments, where individuals must simultaneously pursue goals and avoid obstacles, presents a significant challenge for the brain. To understand the underlying mechanisms of action selection in such contexts, we propose a computational model that extends stochastic optimal control theory through a novel framework for obstacle avoidance. The model decomposes action selection as a we...

Topics

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• Catechol O-Methyltransferase
• Learning
• Memory, Short-Term
• Models, Neurological
• Photic Stimulation
• Psychomotor Performance
• Receptors, G-Protein-Coupled
• Reinforcement, Psychology

Identifiers and source

Literature Corpus work

c4353c7b-1c8d-5f41-8e28-cd8c5597d0b7

DOI

10.1101/2025.11.17.688790