September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Sequentially dependent errors generalize across naturalistic mammogram stimuli
Author Affiliations & Notes
  • Cristina Ghirardo
    University of California, Berkeley
  • Zhihang Ren
    University of California, Berkeley
  • Zixuan Wang
    University of California, Berkeley
  • Mauro Manassi
    University of California, Berkeley
    University of Aberdeen, Kings College, Aberdeen, UK
  • Min Zhou
    The First People's Hospital of Shuangliu District, Chengdu
  • David Whitney
    University of California, Berkeley
  • Footnotes
    Acknowledgements  National Institutes of Health grant 5R01CA236793-02
Journal of Vision September 2021, Vol.21, 2231. doi:https://doi.org/10.1167/jov.21.9.2231
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      Cristina Ghirardo, Zhihang Ren, Zixuan Wang, Mauro Manassi, Min Zhou, David Whitney; Sequentially dependent errors generalize across naturalistic mammogram stimuli. Journal of Vision 2021;21(9):2231. doi: https://doi.org/10.1167/jov.21.9.2231.

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

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Abstract

Radiologists rely on visual search to locate and identify lesions in mammograms. An underlying assumption in radiology is that the perception of each mammogram is free from the influence of preceding images; however, that may not be the case. Serial dependence, the tendency for the visual system to represent images as more similar to those previously viewed, occurs most frequently between ambiguous stimuli just like those found in radiological screening. (Cicchini, et al., PNAS, 2014; Fischer & Whitney, Nature Neuro, 2014; Liberman et al., Curr Bio, 2014; Kiyonaga et al., TiCS, 2017). Recent work has shown radiologists’ perception of simulated tumors is biased toward previously seen stimuli (Manassi et al., Sci Reports, 2019; Ghirardo et. al., VSS, 2020). This serial dependence could cause diagnostic errors; however, previous work on this hypothesis was limited to artificial, unrealistic stimuli. To overcome these limitations, we used a generative adversarial network to create naturalistic simulated mammogram images via interpolation (Ren et al., VSS, 2020), which radiologists misclassified as real mammograms. From these, we created sets of similar simulated mammograms. Using these as stimuli in a standard serial dependence experiment, untrained observers viewed a random simulated mammogram on each trial and subsequently matched the mammogram using continuous report. We found serial dependence with all of the simulated radiographs: the reported mammograms were pulled ~9-12% toward those previously seen. The effect extended back at least two trials (~10 sec). These findings suggest that serial dependence extends to realistic radiographs, and that it may contribute to some of the misdiagnoses found in radiological practice.

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