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Category effects on stimulus estimation: Shifting and skewed frequency distributions—A reexamination

Overview of attention for article published in Psychonomic Bulletin & Review, October 2017
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Title
Category effects on stimulus estimation: Shifting and skewed frequency distributions—A reexamination
Published in
Psychonomic Bulletin & Review, October 2017
DOI 10.3758/s13423-017-1392-7
Pubmed ID
Authors

Sean Duffy, John Smith

Abstract

Duffy, Huttenlocher, Hedges, and Crawford (Psychonomic Bulletin & Review, 17(2), 224-230, 2010) report on experiments where participants estimate the lengths of lines. These studies were designed to test the category adjustment model (CAM), a Bayesian model of judgments. The authors report that their analysis provides evidence consistent with CAM: that there is a bias toward the running mean and not recent stimuli. We reexamine their data. First, we attempt to replicate their analysis, and we obtain different results. Second, we conduct a different statistical analysis. We find significant recency effects, and we identify several specifications where the running mean is not significantly related to judgment. Third, we conduct tests of auxiliary predictions of CAM. We do not find evidence that the bias toward the mean increases with exposure to the distribution. We also do not find that responses longer than the maximum of the distribution or shorter than the minimum become less likely with greater exposure to the distribution. Fourth, we produce a simulated dataset that is consistent with key features of CAM, and our methods correctly identify it as consistent with CAM. We conclude that the Duffy et al. (2010) dataset is not consistent with CAM. We also discuss how conventions in psychology do not sufficiently reduce the likelihood of these mistakes in future research. We hope that the methods that we employ will be used to evaluate other datasets.

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Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 12 46%
Student > Master 3 12%
Researcher 3 12%
Lecturer > Senior Lecturer 2 8%
Student > Bachelor 1 4%
Other 2 8%
Unknown 3 12%
Readers by discipline Count As %
Psychology 14 54%
Computer Science 2 8%
Business, Management and Accounting 1 4%
Linguistics 1 4%
Physics and Astronomy 1 4%
Other 2 8%
Unknown 5 19%