Abstract

ABSTRACTThe time loss bias describes overestimation of time lost after speed decreases from high speeds and underestimations after decreases from low driving speeds. Participants judged the speed decrease from one speed (e.g. 130 km/h) that would give the same time loss as a decrease from another speed (e.g. from 40 to 30 km/h). We carried out descriptive spectral analyses of distributions of judgments for each problem. Each distribution peak was associated with a judgment rule. The first study found two different judgment processes both leading to the time loss bias: a Difference process rule used for 20% and a Ratio rule used for 31% of the judgments. The correct rule applied to 10% of the judgments. The second study added verbal protocols. The results showed that the Ratio rule was most common (41%) followed by the Difference (12%) and correct (8%) rules. Verbal reports supported these results.

Highlights

  • The purpose of this study is twofold

  • 1We did not solve this equation for JVB2 because the results showed us that this would not assist in understanding the rules used by the participants. 2Note, a Ratio rule may reflect different judgment rules, but all include the ratios between the speeds (Svenson, 1970). 3We thank a reviewer who made us aware of this similarity

  • Spectral analysis is sensitive to individual differences and well suited for conjoint analysis with verbal protocols, a procedure suggested by several authors (Ranyard & Svenson, 2019; Schulte-Mecklenbeck et al, 2019)

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Summary

Introduction

The purpose of this study is twofold. First, we aim at describing the cognitive processes that produce the time loss bias (Svenson & Treurniet, 2017). We will show the usefulness of analysis of judgment distributions for detecting and describing different cognitive processes that produce a human judgment. Cognitive heuristics or simplifying cognitive rules enable people to judge relationships which they understand very poorly or not at all, but think that they can judge. Even if they are often valuable (Gigerenzer & Todd, 1999a, 1999b), heuristics can lead to systematic biases (Cohen et al, 1956; Gilovich et al, 2002; Johnson-Laird, 1999; Kahneman & Tversky, 1982; Montibeller & Winterfeldt, 2015; 1979; Wikipedia, 2019)

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