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Published May 2007 | public
Book Section - Chapter

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


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.

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.

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