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Model-Based fMRI and Its Application to Reward Learning and Decision Making

O'Doherty, John P. and Hampton, Alan and Kim, Hackjin (2007) Model-Based fMRI and Its Application to Reward Learning and Decision Making. In: Reward and decision making in corticobasal ganglia networks. Annals of the New York Academy of Sciences. No.1104. Blackwell , Boston. MA, pp. 35-53. ISBN 978-1-57331-674-3. https://resolver.caltech.edu/CaltechAUTHORS:20101018-111343714

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Abstract

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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
http://dx.doi.org/10.1196/annals.1390.022DOIUNSPECIFIED
http://onlinelibrary.wiley.com/doi/10.1196/annals.1390.022/abstract;jsessionid=147D75BFA1AB1E77E6A93DB94FB53343.d01t01PublisherUNSPECIFIED
ORCID:
AuthorORCID
O'Doherty, John P.0000-0003-0016-3531
Additional Information:© 2007 New York Academy of Sciences. Article first published online: 20 Jun. 2007. This work was funded by grants from the Gimbel Discovery Fund for Neuroscience, the Gordon and Betty Moore Foundation, and a Searle Scholarship to JOD.We would like to thank Nathaniel Daw, Peter Dayan, Ray Dolan, Karl Friston, and Ben Seymour at UCL, and Peter Bossaerts and Shin Shimojo at Caltech, who were major collaborators on some of the research studies described here.
Funders:
Funding AgencyGrant Number
Gimbel Discovery Fund for NeuroscienceUNSPECIFIED
Gordon and Betty Moore FoundationUNSPECIFIED
Searle ScholarshipUNSPECIFIED
Subject Keywords:computational models; neuroimaging; prediction error; expected value; conditioning; striatum; ventromedial prefrontal cortex
Series Name:Annals of the New York Academy of Sciences
Issue or Number:1104
DOI:10.1196/annals.1390.022
Record Number:CaltechAUTHORS:20101018-111343714
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20101018-111343714
Official Citation:O'DOHERTY, J. P., HAMPTON, A. and KIM, H. (2007), Model-Based fMRI and Its Application to Reward Learning and Decision Making. Annals of the New York Academy of Sciences, 1104: 35–53. doi: 10.1196/annals.1390.022
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:20450
Collection:CaltechAUTHORS
Deposited By: Jason Perez
Deposited On:26 Oct 2010 20:58
Last Modified:08 Nov 2021 23:59

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