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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Autores principales:	Jill X O'Reilly, Saad Jbabdi, Matthew F S Rushworth, Timothy E J Behrens
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/25838aef1ea2461a81c44604b3853e0f
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spelling	oai:doaj.org-article:25838aef1ea2461a81c44604b3853e0f2021-11-18T05:37:49ZBrain systems for probabilistic and dynamic prediction: computational specificity and integration.1544-91731545-788510.1371/journal.pbio.1001662https://doaj.org/article/25838aef1ea2461a81c44604b3853e0f2013-09-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24086106/pdf/?tool=EBIhttps://doaj.org/toc/1544-9173https://doaj.org/toc/1545-7885A 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.Jill X O'ReillySaad JbabdiMatthew F S RushworthTimothy E J BehrensPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Biology, Vol 11, Iss 9, p e1001662 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Jill X O'Reilly Saad Jbabdi Matthew F S Rushworth Timothy E J Behrens Brain systems for probabilistic and dynamic prediction: computational specificity and integration.
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.
format	article
author	Jill X O'Reilly Saad Jbabdi Matthew F S Rushworth Timothy E J Behrens
author_facet	Jill X O'Reilly Saad Jbabdi Matthew F S Rushworth Timothy E J Behrens
author_sort	Jill X O'Reilly
title	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_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_sort	brain systems for probabilistic and dynamic prediction: computational specificity and integration.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/25838aef1ea2461a81c44604b3853e0f
work_keys_str_mv	AT jillxoreilly brainsystemsforprobabilisticanddynamicpredictioncomputationalspecificityandintegration AT saadjbabdi brainsystemsforprobabilisticanddynamicpredictioncomputationalspecificityandintegration AT matthewfsrushworth brainsystemsforprobabilisticanddynamicpredictioncomputationalspecificityandintegration AT timothyejbehrens brainsystemsforprobabilisticanddynamicpredictioncomputationalspecificityandintegration
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Brain systems for probabilistic and dynamic prediction: computational specificity and integration.

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