October 2020
Volume 20, Issue 11
Open Access
Vision Sciences Society Annual Meeting Abstract  |   October 2020
Prevalence effects on perceptual decisions: Category broadening, elevated miss rates, or both?
Author Affiliations & Notes
  • Wanyi Lyu
    Brigham & Women's Hospital
  • David E. Levari
    Harvard Business School
  • Makaela S. Nartker
    Johns Hopkins University
  • Daniel S. Little
    Bowdoin College
  • Jeremy M. Wolfe
    Brigham & Women's Hospital
  • Footnotes
    Acknowledgements  NEI EY017001
Journal of Vision October 2020, Vol.20, 720. doi:https://doi.org/10.1167/jov.20.11.720
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      Wanyi Lyu, David E. Levari, Makaela S. Nartker, Daniel S. Little, Jeremy M. Wolfe; Prevalence effects on perceptual decisions: Category broadening, elevated miss rates, or both?. Journal of Vision 2020;20(11):720. doi: https://doi.org/10.1167/jov.20.11.720.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

How does the prevalence of the target influence perceptual decisions? Levari et al (2018) made 2AFC decisions as to whether a dot on a blue-purple continuum was blue. When blue items were less prevalent, observers were more likely to call ambiguous stimuli “blue”. They expanded their concept of blue (more liberal criterion). In contrast, Wolfe et al. find that observers become more conservative at low prevalence and find fewer targets in visual search experiments. What determines whether observers’ decision criteria become more liberal or more conservative when prevalence is low? In a series of replications of Levari’s study, we tested the effect of the response type (2AFC vs. Go/No-Go), color (blue-purple vs. red-green continua), stimuli type (solid color vs texture), and trial-by-trial feedback. Feedback appears to be the critical variable. In the presence of feedback, observers become more conservative at low prevalence. In the absence of feedback, they become more liberal, broadening the definition of less common target categories. Similar results were obtained with a shape continuum from rounded (“Bouba”) to spiky (“Kiki”). When Boubas are rare, ambiguous stimuli are called “Bouba” when there is no feedback. They are more likely to be called “Kiki” when there is feedback. There appear to be two, prevalence-based forces pushing and pulling decision criteria. These effects of prevalence have practical implications for low prevalence tasks like cancer screening. Do the pressures of prevalence cause experts to incorrectly categorize ambiguous stimuli as abnormal? In other work, multiple labs have shown that low prevalence causes experts to miss more targets. We will discuss whether category broadening and elevated miss errors might (unfortunately) coexist at low prevalence.

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