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Published August 9, 2006 | Published + Supplemental Material
Journal Article Open

Role of the Ventromedial Prefrontal Cortex in Abstract State-Based Inference during Decision Making in Humans


Many real-life decision-making problems incorporate higher-order structure, involving interdependencies between different stimuli, actions, and subsequent rewards. It is not known whether brain regions implicated in decision making, such as the ventromedial prefrontal cortex (vmPFC), use a stored model of the task structure to guide choice (model-based decision making) or merely learn action or state values without assuming higher-order structure as in standard reinforcement learning. To discriminate between these possibilities, we scanned human subjects with functional magnetic resonance imaging while they performed a simple decision-making task with higher-order structure, probabilistic reversal learning. We found that neural activity in a key decision-making region, the vmPFC, was more consistent with a computational model that exploits higher-order structure than with simple reinforcement learning. These results suggest that brain regions, such as the vmPFC, use an abstract model of task structure to guide behavioral choice, computations that may underlie the human capacity for complex social interactions and abstract strategizing.

Additional Information

© 2006 Society for Neuroscience. Received March 7, 2006; revised July 7, 2006; accepted July 8, 2006. This work was supported by California Institute of Technology start-up funds to J.P.O. from the Gimbel Discovery Fund in Neuroscience. A.N.H. was partly supported by the Physical Sciences Division of the National Aeronautics and Space Administration Office of Biological and Physical Research. We thank Nathaniel Daw for helpful comments on this manuscript. We also thank Steve Flaherty and Mike Tyzska for technical and MRI support.

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Supplemental Material - Figure-3.gif

Supplemental Material - SuppFig1.gif

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Supplemental Material - SupplementaryData2.pdf


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August 22, 2023
October 18, 2023