Abstract
Serial Dependence is a ubiquitous visual phenomenon in which sequentially viewed images appear more similar than they actually are, thus facilitating an efficient and stable perceptual experience in human observers. Although serial dependence is adaptive and beneficial in the naturally autocorrelated visual world, a smoothing perceptual experience, it might turn maladaptive in artificial circumstances, such as medical image perception tasks, where visual stimuli are randomly sequenced. Here, we analyzed 758,139 skin cancer diagnostic records from an online app, and we quantified the semantic similarity between sequential dermatology images using a computer vision model as well as human raters. We then tested whether serial dependence in perception occurs in dermatological judgments as a function of image similarity. We found significant serial dependence in perceptual discrimination judgments of lesion malignancy. Moreover, the serial dependence was tuned to the similarity in the images, and it decayed over time. The results indicate that relatively realistic store-and-forward dermatology judgments may be biased by serial dependence. These findings help in understanding one potential source of systematic bias and errors in medical image perception tasks and hint at useful approaches that could alleviate the errors due to serial dependence.
Original language | English |
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Article number | 1775 |
Number of pages | 14 |
Journal | Diagnostics |
Volume | 13 |
Issue number | 10 |
DOIs | |
Publication status | Published - 17 May 2023 |
Bibliographical note
This research was funded by the National Institutes of Health (NIH) grant number R01CA236793.Data Availability Statement
n/aKeywords
- serial dependence
- semantic similarity
- computer vision
- medical image perception
- skin cancer diagnostic
- systematic error