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Empirical evidence for resource-rational anchoring and adjustment

Overview of attention for article published in Psychonomic Bulletin & Review, May 2017
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Title
Empirical evidence for resource-rational anchoring and adjustment
Published in
Psychonomic Bulletin & Review, May 2017
DOI 10.3758/s13423-017-1288-6
Pubmed ID
Authors

Falk Lieder, Thomas L. Griffiths, Quentin J. M. Huys, Noah D. Goodman

Abstract

People's estimates of numerical quantities are systematically biased towards their initial guess. This anchoring bias is usually interpreted as sign of human irrationality, but it has recently been suggested that the anchoring bias instead results from people's rational use of their finite time and limited cognitive resources. If this were true, then adjustment should decrease with the relative cost of time. To test this hypothesis, we designed a new numerical estimation paradigm that controls people's knowledge and varies the cost of time and error independently while allowing people to invest as much or as little time and effort into refining their estimate as they wish. Two experiments confirmed the prediction that adjustment decreases with time cost but increases with error cost regardless of whether the anchor was self-generated or provided. These results support the hypothesis that people rationally adapt their number of adjustments to achieve a near-optimal speed-accuracy tradeoff. This suggests that the anchoring bias might be a signature of the rational use of finite time and limited cognitive resources rather than a sign of human irrationality.

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The data shown below were compiled from readership statistics for 80 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 80 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 21 26%
Researcher 10 13%
Student > Bachelor 9 11%
Student > Master 9 11%
Lecturer 4 5%
Other 9 11%
Unknown 18 23%
Readers by discipline Count As %
Psychology 24 30%
Neuroscience 8 10%
Computer Science 6 8%
Business, Management and Accounting 4 5%
Mathematics 4 5%
Other 14 18%
Unknown 20 25%