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Learning and Generalization under Ambiguity: An fMRI Study

Overview of attention for article published in PLoS Computational Biology, January 2012
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Title
Learning and Generalization under Ambiguity: An fMRI Study
Published in
PLoS Computational Biology, January 2012
DOI 10.1371/journal.pcbi.1002346
Pubmed ID
Authors

J. R. Chumbley, G. Flandin, D. R. Bach, J. Daunizeau, E. Fehr, R. J. Dolan, K. J. Friston

Abstract

Adaptive behavior often exploits generalizations from past experience by applying them judiciously in new situations. This requires a means of quantifying the relative importance of prior experience and current information, so they can be balanced optimally. In this study, we ask whether the brain generalizes in an optimal way. Specifically, we used Bayesian learning theory and fMRI to test whether neuronal responses reflect context-sensitive changes in ambiguity or uncertainty about experience-dependent beliefs. We found that the hippocampus expresses clear ambiguity-dependent responses that are associated with an augmented rate of learning. These findings suggest candidate neuronal systems that may be involved in aberrations of generalization, such as over-confidence.

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The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 142 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 3%
France 4 3%
United States 2 1%
Japan 2 1%
Netherlands 1 <1%
Italy 1 <1%
Belgium 1 <1%
Germany 1 <1%
Switzerland 1 <1%
Other 1 <1%
Unknown 124 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 24%
Researcher 28 20%
Student > Master 14 10%
Student > Bachelor 12 8%
Student > Doctoral Student 10 7%
Other 35 25%
Unknown 9 6%
Readers by discipline Count As %
Psychology 42 30%
Neuroscience 28 20%
Agricultural and Biological Sciences 18 13%
Computer Science 9 6%
Social Sciences 7 5%
Other 24 17%
Unknown 14 10%