Learning and embodied decisions in active inference

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 21, 2024

Abstract Biological organisms constantly face the necessity to act timely in dynamic environments and balance choice accuracy against risk of missing valid opportunities. As formalized by embodied decision models, this might require brain architectures wherein decision-making motor control interact reciprocally, stark contrast traditional models that view them as serial processes. Previous studies have assessed dynamics emerge naturally under active inference – a computational paradigm considers action perception subject same imperative free energy minimization. In particular, agents can infer their targets using own movements (and not only external sensations) evidence, i.e., via self-evidencing . Such shown appropriate conditions, action-generated feedback stabilize improve However, how adaptation internal environmental contingencies influences decisions is yet be addressed. To shed light on challenge, study we systematically investigate learning an model during two-alternative forced task, hybrid (discrete continuous) framework. Our results show adapt contexts various statistical regularities task namely, prior preferences for correct target, cue validity, response strategies prioritize faster or slower (but more accurate) decisions. Crucially, these illustrate efficacy discrete sensorimotor from continuous dynamics.

Language: Английский

Cross-Modal Motor Representation Learning

2022 International Joint Conference on Neural Networks (IJCNN), Journal Year: 2024, Volume and Issue: unknown, P. 1 - 8

Published: June 30, 2024

Language: Английский

Learning and Embodied Decisions in Active Inference

Communications in computer and information science, Journal Year: 2024, Volume and Issue: unknown, P. 72 - 87

Published: Dec. 30, 2024

Language: Английский