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Functional brain networks for learning predictive statistics

Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the fun...

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Autores principales: Giorgio, Joseph, Karlaftis, Vasilis M., Wang, Rui, Shen, Yuan, Tino, Peter, Welchman, Andrew, Kourtzi, Zoe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Masson 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181801/
https://www.ncbi.nlm.nih.gov/pubmed/28923313
http://dx.doi.org/10.1016/j.cortex.2017.08.014
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author Giorgio, Joseph
Karlaftis, Vasilis M.
Wang, Rui
Shen, Yuan
Tino, Peter
Welchman, Andrew
Kourtzi, Zoe
author_facet Giorgio, Joseph
Karlaftis, Vasilis M.
Wang, Rui
Shen, Yuan
Tino, Peter
Welchman, Andrew
Kourtzi, Zoe
author_sort Giorgio, Joseph
collection PubMed
description Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics.
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spelling pubmed-61818012018-10-15 Functional brain networks for learning predictive statistics Giorgio, Joseph Karlaftis, Vasilis M. Wang, Rui Shen, Yuan Tino, Peter Welchman, Andrew Kourtzi, Zoe Cortex Article Making predictions about future events relies on interpreting streams of information that may initially appear incomprehensible. This skill relies on extracting regular patterns in space and time by mere exposure to the environment (i.e., without explicit feedback). Yet, we know little about the functional brain networks that mediate this type of statistical learning. Here, we test whether changes in the processing and connectivity of functional brain networks due to training relate to our ability to learn temporal regularities. By combining behavioral training and functional brain connectivity analysis, we demonstrate that individuals adapt to the environment's statistics as they change over time from simple repetition to probabilistic combinations. Further, we show that individual learning of temporal structures relates to decision strategy. Our fMRI results demonstrate that learning-dependent changes in fMRI activation within and functional connectivity between brain networks relate to individual variability in strategy. In particular, extracting the exact sequence statistics (i.e., matching) relates to changes in brain networks known to be involved in memory and stimulus-response associations, while selecting the most probable outcomes in a given context (i.e., maximizing) relates to changes in frontal and striatal networks. Thus, our findings provide evidence that dissociable brain networks mediate individual ability in learning behaviorally-relevant statistics. Masson 2018-10 /pmc/articles/PMC6181801/ /pubmed/28923313 http://dx.doi.org/10.1016/j.cortex.2017.08.014 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Giorgio, Joseph
Karlaftis, Vasilis M.
Wang, Rui
Shen, Yuan
Tino, Peter
Welchman, Andrew
Kourtzi, Zoe
Functional brain networks for learning predictive statistics
title Functional brain networks for learning predictive statistics
title_full Functional brain networks for learning predictive statistics
title_fullStr Functional brain networks for learning predictive statistics
title_full_unstemmed Functional brain networks for learning predictive statistics
title_short Functional brain networks for learning predictive statistics
title_sort functional brain networks for learning predictive statistics
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6181801/
https://www.ncbi.nlm.nih.gov/pubmed/28923313
http://dx.doi.org/10.1016/j.cortex.2017.08.014
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