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Functional brain networks for learning predictive statistics
Giorgio, Joseph1; Karlaftis, Vasilis M.1; Wang, Rui1,2; Shen, Yuan3,4; Tino, Peter4; Welchman, Andrew1; Kourtzi, Zoe1
First AuthorGiorgio, Joseph
Source PublicationCORTEX
Correspondent (z. kourtzi)

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. (C) 2017 The Authors. Published by Elsevier Ltd.

KeywordBrain Plasticity Fmri Functional Network Connectivity Individual Differences Statistical Learning
Indexed BySCI
Funding ProjectEngineering and Physical Sciences Research Council[EP/L000296/1] ; Wellcome Trust[095183/Z/10/Z] ; European Community[PITN-GA-2011-290011] ; European Community[PITN-GA-2012-316746] ; Leverhulme Trust[RF-2011-378] ; Biotechnology and Biological Sciences Research Council[H012508] ; Biotechnology and Biological Sciences Research Council[H012508] ; Leverhulme Trust[RF-2011-378] ; European Community[PITN-GA-2012-316746] ; European Community[PITN-GA-2011-290011] ; Wellcome Trust[095183/Z/10/Z] ; Engineering and Physical Sciences Research Council[EP/L000296/1]
WOS Research AreaBehavioral Sciences ; Neurosciences & Neurology
WOS SubjectBehavioral Sciences ; Neurosciences
WOS IDWOS:000448092300018
WOS KeywordMedial Temporal-lobe ; Prefrontal Cortex ; Individual Variability ; Memory Retrieval ; Visual-attention ; Episodic Memory ; Neural Circuits ; Working-memory ; Basal Ganglia ; Implicit
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Corresponding AuthorKourtzi, Zoe
Affiliation1.Univ Cambridge, Dept Psychol, Cambridge, England
2.Chinese Acad Sci, Inst Psychol, Key Lab Mental Hlth, Beijing, Peoples R China
3.Xian Jiaotong Liverpool Univ, Dept Math Sci, Suzhou, Peoples R China
4.Univ Birmingham, Sch Comp Sci, Birmingham, W Midlands, England
Recommended Citation
GB/T 7714
Giorgio, Joseph,Karlaftis, Vasilis M.,Wang, Rui,et al. Functional brain networks for learning predictive statistics[J]. CORTEX,2018,107:204-219.
APA Giorgio, Joseph.,Karlaftis, Vasilis M..,Wang, Rui.,Shen, Yuan.,Tino, Peter.,...&Kourtzi, Zoe.(2018).Functional brain networks for learning predictive statistics.CORTEX,107,204-219.
MLA Giorgio, Joseph,et al."Functional brain networks for learning predictive statistics".CORTEX 107(2018):204-219.
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