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Determinants of linear judgment: A meta-analysis of lens model studies

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  • Natalia Karelaia
  • Robin Hogarth

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Abstract

The mathematical representation of Brunswik’s lens model has been used extensively to study human judgment and provides a unique opportunity to conduct a meta-analysis of studies that covers roughly five decades. Specifically, we analyze statistics of the “lens model equation” (Tucker, 1964) associated with 259 different task environments obtained from 78 papers. In short, we find – on average – fairly high levels of judgmental achievement and note that people can achieve similar levels of cognitive performance in both noisy and predictable environments. Although overall performance varies little between laboratory and field studies, both differ in terms of components of performance and types of environments (numbers of cues and redundancy). An analysis of learning studies reveals that the most effective form of feedback is information about the task. We also analyze empirically when bootstrapping is more likely to occur. We conclude by indicating shortcomings of the kinds of studies conducted to date, limitations in the lens model methodology, and possibilities for future research.

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File URL: http://www.econ.upf.edu/docs/papers/downloads/1007.pdf
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Bibliographic Info

Paper provided by Department of Economics and Business, Universitat Pompeu Fabra in its series Economics Working Papers with number 1007.

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Date of creation: Feb 2007
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Handle: RePEc:upf:upfgen:1007

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Web page: http://www.econ.upf.edu/

Related research

Keywords: Judgment; lens model; linear models; learning; bootstrapping;

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Cited by:
  1. Marc Jekel & Susann Fiedler & Andreas Glockner, 2011. "Diagnostic task selection for strategy classification in judgment and decision making," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 782-799, December.
  2. Andreas Glockner & Tilmann Betsch, 2011. "The Empirical content of theories in judgment and decision making: Shortcomings and remedies," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 711-721, December.
  3. Yoav Ganzach, 2009. "Coherence and correspondence in the psychological analysis of numerical predictions: How error-prone heuristics are replaced by ecologically valid heuristics," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 4(2), pages 175-185, March.
  4. Gueorgui I. Kolev & Robin Hogarth, 2008. "Illusory correlation in the remuneration of chief executive officers: It pays to play golf, and well," Economics Working Papers 1132, Department of Economics and Business, Universitat Pompeu Fabra.
  5. Frank Renkewitz & Heather M. Fuchs & Susann Fiedler, 2011. "Is there evidence of publication biases in JDM research?," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 870-881, December.

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