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Model‐Based fMRI and Its Application to Reward Learning and Decision Making
Author(s) -
O'DOHERTY JOHN P.,
HAMPTON ALAN,
KIM HACKJIN
Publication year - 2007
Publication title -
annals of the new york academy of sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.712
H-Index - 248
eISSN - 1749-6632
pISSN - 0077-8923
DOI - 10.1196/annals.1390.022
Subject(s) - functional magnetic resonance imaging , computer science , neuroimaging , cognition , process (computing) , artificial intelligence , computational model , machine learning , task (project management) , action (physics) , encoding (memory) , cognitive neuroscience , functional neuroimaging , psychology , neuroscience , physics , management , quantum mechanics , economics , operating system
In model‐based functional magnetic resonance imaging (fMRI), signals derived from a computational model for a specific cognitive process are correlated against fMRI data from subjects performing a relevant task to determine brain regions showing a response profile consistent with that model. A key advantage of this technique over more conventional neuroimaging approaches is that model‐based fMRI can provide insights into how a particular cognitive process is implemented in a specific brain area as opposed to merely identifying where a particular process is located. This review will briefly summarize the approach of model‐based fMRI, with reference to the field of reward learning and decision making, where computational models have been used to probe the neural mechanisms underlying learning of reward associations, modifying action choice to obtain reward, as well as in encoding expected value signals that reflect the abstract structure of a decision problem. Finally, some of the limitations of this approach will be discussed .