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A neural active inference model of perceptual-motor learning

The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of actio...

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Autores principales: Yang, Zhizhuo, Diaz, Gabriel J., Fajen, Brett R., Bailey, Reynold, Ororbia, Alexander G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986490/
https://www.ncbi.nlm.nih.gov/pubmed/36890967
http://dx.doi.org/10.3389/fncom.2023.1099593
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author Yang, Zhizhuo
Diaz, Gabriel J.
Fajen, Brett R.
Bailey, Reynold
Ororbia, Alexander G.
author_facet Yang, Zhizhuo
Diaz, Gabriel J.
Fajen, Brett R.
Bailey, Reynold
Ororbia, Alexander G.
author_sort Yang, Zhizhuo
collection PubMed
description The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans through the systematic investigation of a visual-motor task that has been well-explored—that of intercepting a target moving over a ground plane. Previous research demonstrated that humans performing this task resorted to anticipatory changes in speed intended to compensate for semi-predictable changes in target speed later in the approach. To capture this behavior, our proposed “neural” AIF agent uses artificial neural networks to select actions on the basis of a very short term prediction of the information about the task environment that these actions would reveal along with a long-term estimate of the resulting cumulative expected free energy. Systematic variation revealed that anticipatory behavior emerged only when required by limitations on the agent's movement capabilities, and only when the agent was able to estimate accumulated free energy over sufficiently long durations into the future. In addition, we present a novel formulation of the prior mapping function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy/reward. Together, these results demonstrate the use of AIF as a plausible model of anticipatory visually guided behavior in humans.
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spelling pubmed-99864902023-03-07 A neural active inference model of perceptual-motor learning Yang, Zhizhuo Diaz, Gabriel J. Fajen, Brett R. Bailey, Reynold Ororbia, Alexander G. Front Comput Neurosci Neuroscience The active inference framework (AIF) is a promising new computational framework grounded in contemporary neuroscience that can produce human-like behavior through reward-based learning. In this study, we test the ability for the AIF to capture the role of anticipation in the visual guidance of action in humans through the systematic investigation of a visual-motor task that has been well-explored—that of intercepting a target moving over a ground plane. Previous research demonstrated that humans performing this task resorted to anticipatory changes in speed intended to compensate for semi-predictable changes in target speed later in the approach. To capture this behavior, our proposed “neural” AIF agent uses artificial neural networks to select actions on the basis of a very short term prediction of the information about the task environment that these actions would reveal along with a long-term estimate of the resulting cumulative expected free energy. Systematic variation revealed that anticipatory behavior emerged only when required by limitations on the agent's movement capabilities, and only when the agent was able to estimate accumulated free energy over sufficiently long durations into the future. In addition, we present a novel formulation of the prior mapping function that maps a multi-dimensional world-state to a uni-dimensional distribution of free-energy/reward. Together, these results demonstrate the use of AIF as a plausible model of anticipatory visually guided behavior in humans. Frontiers Media S.A. 2023-02-20 /pmc/articles/PMC9986490/ /pubmed/36890967 http://dx.doi.org/10.3389/fncom.2023.1099593 Text en Copyright © 2023 Yang, Diaz, Fajen, Bailey and Ororbia. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Yang, Zhizhuo
Diaz, Gabriel J.
Fajen, Brett R.
Bailey, Reynold
Ororbia, Alexander G.
A neural active inference model of perceptual-motor learning
title A neural active inference model of perceptual-motor learning
title_full A neural active inference model of perceptual-motor learning
title_fullStr A neural active inference model of perceptual-motor learning
title_full_unstemmed A neural active inference model of perceptual-motor learning
title_short A neural active inference model of perceptual-motor learning
title_sort neural active inference model of perceptual-motor learning
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9986490/
https://www.ncbi.nlm.nih.gov/pubmed/36890967
http://dx.doi.org/10.3389/fncom.2023.1099593
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