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Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration

A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictiv...

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Detalles Bibliográficos
Autores principales: O'Reilly, Jill X., Jbabdi, Saad, Rushworth, Matthew F. S., Behrens, Timothy E. J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3782423/
https://www.ncbi.nlm.nih.gov/pubmed/24086106
http://dx.doi.org/10.1371/journal.pbio.1001662
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author O'Reilly, Jill X.
Jbabdi, Saad
Rushworth, Matthew F. S.
Behrens, Timothy E. J.
author_facet O'Reilly, Jill X.
Jbabdi, Saad
Rushworth, Matthew F. S.
Behrens, Timothy E. J.
author_sort O'Reilly, Jill X.
collection PubMed
description A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI) we showed that activity in two brain systems (typically associated with reward learning and motor control) could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output.
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spelling pubmed-37824232013-10-01 Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration O'Reilly, Jill X. Jbabdi, Saad Rushworth, Matthew F. S. Behrens, Timothy E. J. PLoS Biol Research Article A computational approach to functional specialization suggests that brain systems can be characterized in terms of the types of computations they perform, rather than their sensory or behavioral domains. We contrasted the neural systems associated with two computationally distinct forms of predictive model: a reinforcement-learning model of the environment obtained through experience with discrete events, and continuous dynamic forward modeling. By manipulating the precision with which each type of prediction could be used, we caused participants to shift computational strategies within a single spatial prediction task. Hence (using fMRI) we showed that activity in two brain systems (typically associated with reward learning and motor control) could be dissociated in terms of the forms of computations that were performed there, even when both systems were used to make parallel predictions of the same event. A region in parietal cortex, which was sensitive to the divergence between the predictions of the models and anatomically connected to both computational networks, is proposed to mediate integration of the two predictive modes to produce a single behavioral output. Public Library of Science 2013-09-24 /pmc/articles/PMC3782423/ /pubmed/24086106 http://dx.doi.org/10.1371/journal.pbio.1001662 Text en © 2013 O'Reilly et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
O'Reilly, Jill X.
Jbabdi, Saad
Rushworth, Matthew F. S.
Behrens, Timothy E. J.
Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
title Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
title_full Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
title_fullStr Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
title_full_unstemmed Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
title_short Brain Systems for Probabilistic and Dynamic Prediction: Computational Specificity and Integration
title_sort brain systems for probabilistic and dynamic prediction: computational specificity and integration
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3782423/
https://www.ncbi.nlm.nih.gov/pubmed/24086106
http://dx.doi.org/10.1371/journal.pbio.1001662
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