September 2021
Volume 21, Issue 9
Open Access
Vision Sciences Society Annual Meeting Abstract  |   September 2021
Visual Hindsight Bias for Mammogram Abnormalities in Expert Radiologists
Author Affiliations
  • Hayden Schill
    University of California, San Diego
  • Timothy Brady
    University of California, San Diego
Journal of Vision September 2021, Vol.21, 2395. doi:
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      Hayden Schill, Timothy Brady; Visual Hindsight Bias for Mammogram Abnormalities in Expert Radiologists. Journal of Vision 2021;21(9):2395.

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

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Hindsight bias – where people believe they can accurately predict something only once they know about it – is a pervasive decision making phenomenon, including in interpretation of radiological images (e.g., Berlin, 2000). However, some evidence suggests it is not only a decision making phenomena but also a visual perception one, where prior information about an image genuinely enhances our visual perception of that image (Harley et al. 2004). For example, the random image structure evolution (RISE) method of Sadr & Sinha (2004), where objects appear in random order embedded in different amounts of noise, demonstrates that primed images are genuinely recognized more easily than those that have not been seen before, a visual hindsight bias. The current experiment tests to what extent expert radiologists show a visual hindsight bias to mammograms with visible abnormalities when they are embedded in noise (e.g., to what extent they see the images differently when they know what the abnormality is, rather than just being biased at a decision level). N=40 experienced mammograph readers were presented with a series of unilateral abnormal mammograms (half had masses, half had calcifications), for three seconds each. After the presentation of each image, observers were asked to rate their confidence on a 6-point scale ranging from confident mass to confident calcification. Critically, we used the RISE method where the images in a sequence were repeated in an unpredictable order, and with varied noise. We found that radiologists who first saw a clear, original image were more accurate in the max noise level condition (d’=0.49) than those who first saw the degraded images (d’=0.20; p=0.017), suggesting that radiologists’ visual perception of medical images is enhanced by prior knowledge of the abnormality. Overall, these results provide preliminary evidence that radiologists experience not only decision level but also visual hindsight bias.


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