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Of monkeys and men: Impatience in perceptual decision-making

Overview of attention for article published in Psychonomic Bulletin & Review, October 2015
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Title
Of monkeys and men: Impatience in perceptual decision-making
Published in
Psychonomic Bulletin & Review, October 2015
DOI 10.3758/s13423-015-0958-5
Pubmed ID
Authors

Udo Boehm, Guy E. Hawkins, Scott Brown, Hedderik van Rijn, Eric-Jan Wagenmakers

Abstract

For decades sequential sampling models have successfully accounted for human and monkey decision-making, relying on the standard assumption that decision makers maintain a pre-set decision standard throughout the decision process. Based on the theoretical argument of reward rate maximization, some authors have recently suggested that decision makers become increasingly impatient as time passes and therefore lower their decision standard. Indeed, a number of studies show that computational models with an impatience component provide a good fit to human and monkey decision behavior. However, many of these studies lack quantitative model comparisons and systematic manipulations of rewards. Moreover, the often-cited evidence from single-cell recordings is not unequivocal and complimentary data from human subjects is largely missing. We conclude that, despite some enthusiastic calls for the abandonment of the standard model, the idea of an impatience component has yet to be fully established; we suggest a number of recently developed tools that will help bring the debate to a conclusive settlement.

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Mendeley readers

The data shown below were compiled from readership statistics for 77 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 3 4%
Netherlands 1 1%
Unknown 73 95%

Demographic breakdown

Readers by professional status Count As %
Student > Master 16 21%
Researcher 14 18%
Student > Ph. D. Student 13 17%
Student > Bachelor 9 12%
Student > Postgraduate 6 8%
Other 11 14%
Unknown 8 10%
Readers by discipline Count As %
Psychology 34 44%
Neuroscience 17 22%
Computer Science 4 5%
Social Sciences 3 4%
Agricultural and Biological Sciences 2 3%
Other 3 4%
Unknown 14 18%