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Cerebral Cortex Advance Access originally published online on May 17, 2006
Cerebral Cortex 2007 17(4):749-759; doi:10.1093/cercor/bhk028
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org

Dynamics of Prefrontal and Cingulate Activity during a Reward-Based Logical Deduction Task

Claire Landmann1, Stanislas Dehaene1,2, Sabina Pappata3, Antoinette Jobert1, Michel Bottlaender4, Dimitri Roumenov4 and Denis Le Bihan5

1 Institut National de la Santé et de la Recherche Médicale 562, Cognitive Neuroimaging, Service Hospitalier Frédéric Joliot, Commissariat à l'Energie Atomique/Département de Recherche Médicale/Direction des Sciences du Vivant, 91401 Orsay Cedex, France, 2 Collège de France, 75231 Paris Cedex05, France, 3 Istituto di Biostrutture e Bioimmagini, Consiglio Nazionale delle Richerche, 80131 Napoli, Italy, 4 Service Hospitalier Frédéric Joliot, Département de Recherche Médicale/Direction des Sciences du Vivant/Commissariat à l'Energie Atomique, Orsay, France, 5 Unité de Neuroimagerie Anatomo-Fonctionnelle, Service Hospitalier Frédéric Joliot, Commissariat à l'Energie Atomique/Direction des Sciences du Vivant, 91401 Orsay Cedex, France

Address correspondence to email: landmannclaire{at}yahoo.fr.

We used behavioral and functional magnetic resonance imaging (fMRI) methods to probe the cerebral organization of a simple logical deduction process. Subjects were engaged in a motor trial-and-error learning task, in which they had to infer the identity of an unknown 4-key code. The design of the task allowed subjects to base their inferences not only on the feedback they received but also on the internal deductions that it afforded (autoevaluation). fMRI analysis revealed a large bilateral parietal, prefrontal, cingulate, and striatal network that activated suddenly during search periods and collapsed during ensuing periods of sequence repetition. Fine-grained analyses of the temporal dynamics of this search network indicated that it operates according to near-optimal rules that include 1) computation of the difference between expected and obtained rewards and 2) anticipatory deductions that predate the actual reception of positive reward. In summary, the dynamics of effortful mental deduction can be tracked with fMRI and relate to a distributed network engaging prefrontal cortex and its interconnected cortical and subcortical regions.

Key Words: autoevaluation • fMRI • learning • prefrontal cortex • reward prediction error


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