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Brain and Behavior in Decision-Making

Overview of attention for article published in PLoS Computational Biology, July 2014
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Title
Brain and Behavior in Decision-Making
Published in
PLoS Computational Biology, July 2014
DOI 10.1371/journal.pcbi.1003700
Pubmed ID
Authors

Peter Cassey, Andrew Heathcote, Scott D. Brown

Abstract

Speed-accuracy tradeoff (SAT) is an adaptive process balancing urgency and caution when making decisions. Computational cognitive theories, known as "evidence accumulation models", have explained SATs via a manipulation of the amount of evidence necessary to trigger response selection. New light has been shed on these processes by single-cell recordings from monkeys who were adjusting their SAT settings. Those data have been interpreted as inconsistent with existing evidence accumulation theories, prompting the addition of new mechanisms to the models. We show that this interpretation was wrong, by demonstrating that the neural spiking data, and the behavioural data are consistent with existing evidence accumulation theories, without positing additional mechanisms. Our approach succeeds by using the neural data to provide constraints on the cognitive model. Open questions remain about the locus of the link between certain elements of the cognitive models and the neurophysiology, and about the relationship between activity in cortical neurons identified with decision-making vs. activity in downstream areas more closely linked with motor effectors.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 104 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 4 4%
United Kingdom 2 2%
Unknown 98 94%

Demographic breakdown

Readers by professional status Count As %
Researcher 23 22%
Student > Ph. D. Student 22 21%
Student > Master 14 13%
Professor 10 10%
Student > Bachelor 8 8%
Other 16 15%
Unknown 11 11%
Readers by discipline Count As %
Psychology 35 34%
Neuroscience 15 14%
Agricultural and Biological Sciences 12 12%
Computer Science 6 6%
Business, Management and Accounting 3 3%
Other 19 18%
Unknown 14 13%