Psychonomic Bulletin & Review

, Volume 25, Issue 2, pp 775–784 | Cite as

Empirical evidence for resource-rational anchoring and adjustment

  • Falk Lieder
  • Thomas L. Griffiths
  • Quentin J. M. Huys
  • Noah D. Goodman
Brief Report

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.

Keywords

Bounded rationality Heuristics Cognitive biases Probabilistic reasoning Anchoring-and-adjustment 

Notes

Acknowledgements

This research was supported by grant number ONR MURI N00014-13-1-0341 from the Office of Naval Research (TLG and NDG), grant number FA-9550-10-1-0232 from the Air Force Office of Scientific Research (TLG), and a John S. McDonnell Scholar Award (NDG).

References

  1. Epley, N, & Gilovich, T (2005). When effortful thinking influences judgmental anchoring: differential effects of forewarning and incentives on self-generated and externally provided anchors. Journal of Behavioral Decision Making, 18(3), 199– 212.CrossRefGoogle Scholar
  2. Epley, N, & Gilovich, T (2006). The anchoring-and-adjustment heuristic. Psychological Science, 17(4), 311–318.CrossRefPubMedGoogle Scholar
  3. Evans, JSB (2008). Dual-processing accounts of reasoning, judgment, and social cognition. Annual Review of Psychology, 59, 255–278.CrossRefPubMedGoogle Scholar
  4. Jacowitz, KE, & Kahneman, D (1995). Measures of anchoring in estimation tasks. Personality and Social Psychology Bulletin, 21(11), 1161–1166.CrossRefGoogle Scholar
  5. Kahneman, D. (2011). Thinking, fast and slow. New York: Farrar, Strauss and Giroux.Google Scholar
  6. Lieder, F, Goodman, ND, & Griffiths, TL. (2013). Reverse-engineering resource-efficient algorithms. Lake Tahoe, USA: Paper presented at NIPS-2013 Workshop Resource-Efficient ML.Google Scholar
  7. Lieder, F, Griffiths, T, Huys, Q, & Goodman, N (2017b). Testing models of anchoring and adjustment. PsyArXiv Preprint. Retrieved from https://osf.io/preprints/psyarxiv/94yvz (Version 3).
  8. Lieder, F, Griffiths, TL, & Goodman, ND (2012). Burn-in, bias, and the rationality of anchoring. In Bartlett, P., Pereira, F. C. N., Bottou, L., Burges, C.J.C., & Weinberger, K. Q. (Eds.), Advances in Neural Information Processing Systems 26. Red Hook: Curran Associates, Inc.Google Scholar
  9. Lieder, F, Griffiths, TL, Huys, QJM, & Goodman, ND (2017a). The anchoring bias reflects rational use of cognitive resources. Psychonomic Bulletin and Review.Google Scholar
  10. Russo, JE, & Schoemaker, PJH (1989). Decision traps: Ten barriers to brilliant decision-making and how to overcome them. Simon and Schuster.Google Scholar
  11. Shenhav, A, Musslick, S, Lieder, F, Kool, W, Griffiths, T, Cohen, J, & Botvinick, M (2017). Toward a rational and mechanistic account of mental effort. Annual Review of Neuroscience, 40.Google Scholar
  12. Simmons, JP, LeBoeuf, RA, & Nelson, LD (2010). The effect of accuracy motivation on anchoring and adjustment: Do people adjust from provided anchors?. Journal of Personality and Social Psychology, 99(6), 917–932.CrossRefPubMedGoogle Scholar
  13. Stanovich, KE (2009). Decision making and rationality in the modern world.Google Scholar
  14. Tversky, A, & Kahneman, D (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124–1131.CrossRefPubMedGoogle Scholar
  15. Zhang, YC, & Schwarz, N (2013). The power of precise numbers: A conversational logic analysis. Journal of Experimental Social Psychology, 49(5), 944–946.CrossRefGoogle Scholar

Copyright information

© Psychonomic Society, Inc. 2017

Authors and Affiliations

  • Falk Lieder
    • 1
    • 2
  • Thomas L. Griffiths
    • 1
    • 5
  • Quentin J. M. Huys
    • 2
    • 4
  • Noah D. Goodman
    • 3
  1. 1.Helen Wills Neuroscience InstituteUniversity of CaliforniaBerkeleyUSA
  2. 2.Translational Neuromodeling Unit, Institute for Biomedical EngineeringUniversity of Zürich and Swiss Federal Institute of Technology (ETH) ZürichZürichSwitzerland
  3. 3.Department of PsychologyStanford UniversityStanfordUSA
  4. 4.Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of PsychiatryUniversity of ZürichZürichSwitzerland
  5. 5.Department of PsychologyUniversity of CaliforniaBerkeleyUSA

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