Determinants of Linear Judgment: A Meta-Analysis of Lens Model Studies

57 Pages Posted: 25 Jul 2007

See all articles by Natalia Karelaia

Natalia Karelaia

INSEAD - Decision Sciences

Robin M. Hogarth

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences

Date Written: February 2007

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.

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

JEL Classification: D81, M10

Suggested Citation

Karelaia, Natalia and Hogarth, Robin M., Determinants of Linear Judgment: A Meta-Analysis of Lens Model Studies (February 2007). Available at SSRN: https://ssrn.com/abstract=1002859 or http://dx.doi.org/10.2139/ssrn.1002859

Natalia Karelaia

INSEAD - Decision Sciences ( email )

United States

Robin M. Hogarth (Contact Author)

Universitat Pompeu Fabra - Faculty of Economic and Business Sciences ( email )

Ramon Trias Fargas 25-27
Barcelona, 08005
Spain
34 93 542 2561 (Phone)
34 93 542 1746 (Fax)

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