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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Masson
2018
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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. |
format | Online Article Text |
id | pubmed-6181801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Masson |
record_format | MEDLINE/PubMed |
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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