Abstract
Using an online, medical image labeling app, 803 individuals rated images of skin lesions as either "melanoma" (skin cancer) or "nevus" (a skin mole). Each block consisted of 80 images. Blocks could have high (50%) or low (20%) target prevalence and could provide full, accurate feedback or no feedback. As in prior work, with feedback, decision criteria were more conservative at low prevalence than at high prevalence and resulted in more miss errors. Without feedback, this low prevalence effect was reversed (albeit, not significantly). Participants could participate in up to four different conditions a day on each of 6 days. Our main interest was in the effect of Block N on Block N + 1. Low prevalence with feedback made participants more conservative on a subsequent block. High prevalence with feedback made participants more liberal on a subsequent block. Conditions with no feedback had no significant impact on the subsequent block. The delay between Blocks 1 and 2 had no significant effect. The effect on the second half of Block 2 was just as large as on the first half. Medical expertise (over the range available in the study) had no impact on these effects, though medical students were better at the task than other groups. Overall, these seem to be robust effects where feedback may be 'teaching' participants how to respond in the future. This might have application in, for example, training or re-training situations.
Highlights
In visual decisions about finding and/or identifying a target, the prevalence of the target makes a difference (Horowitz, 2017)
We know that what you have seen influences what you will report seeing for example, in classic adaptation effects (e.g., Helson, 1964) or in serial dependence effects like those studied by Fischer and Whitney (2014) and many others (e.g., Gekas et al, 2019 or Manassi et al, 2021 for work with radiologists)
What is the effect of the prevalence in one block on performance on a subsequent block?
Summary
In visual decisions about finding and/or identifying a target, the prevalence of the target makes a difference (Horowitz, 2017). The effects of prevalence are of more than academic interest because target prevalence can vary dramatically across tasks in the real world. In a task like identifying signs of breast cancer in mammographic images, the prevalence is very low in a breast cancer screening program where cancer might be present. We know that what you have seen influences what you will report seeing for example, in classic adaptation effects (e.g., Helson, 1964) or in serial dependence effects like those studied by Fischer and Whitney (2014) and many others (e.g., Gekas et al, 2019 or Manassi et al, 2021 for work with radiologists). What is the effect of the prevalence in one block on performance on a subsequent block?
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