Jessica K Witt1, Amelia C Warden1, Michael D Dodd2, Elizabeth E Edney3. 1. Colorado State University, Department of Psychology, Fort Collins, Colorado, United States. 2. University of Nebraska-Lincoln, Department of Psychology, Lincoln, Nebraska, United States. 3. University of Nebraska Medical Center, Omaha, Nebraska, United States.
Abstract
Purpose: Diagnosing breast cancer based on the distribution of calcifications is a visual task and thus prone to visual biases. We tested whether a recently discovered visual bias that has implications for breast cancer diagnosis would be present in expert radiologists, thereby validating the concern of this bias for accurate diagnoses. Approach: We ran a vision experiment with expert radiologists and untrained observers to test the presence of visual bias when judging the spread of dots that resembled calcifications and when judging the spread of line orientations. We calculated visual bias scores for both groups for both tasks. Results: Participants overestimated the spread of the dots and the spread of the line orientations. This bias, referred to as the variability overestimation effect, was of similar magnitudes in both expert radiologists and untrained observers. Even though the radiologists were better at both tasks, they were similarly biased compared with the untrained observers. Conclusions: The results justify the concern of the variability overestimation effect for accurate diagnoses based on breast calcifications. Specifically, the bias is likely to lead to an increased number of false-negative results, thereby leading to delayed treatments.
Purpose: Diagnosing breast cancer based on the distribution of calcifications is a visual task and thus prone to visual biases. We tested whether a recently discovered visual bias that has implications for breast cancer diagnosis would be present in expert radiologists, thereby validating the concern of this bias for accurate diagnoses. Approach: We ran a vision experiment with expert radiologists and untrained observers to test the presence of visual bias when judging the spread of dots that resembled calcifications and when judging the spread of line orientations. We calculated visual bias scores for both groups for both tasks. Results: Participants overestimated the spread of the dots and the spread of the line orientations. This bias, referred to as the variability overestimation effect, was of similar magnitudes in both expert radiologists and untrained observers. Even though the radiologists were better at both tasks, they were similarly biased compared with the untrained observers. Conclusions: The results justify the concern of the variability overestimation effect for accurate diagnoses based on breast calcifications. Specifically, the bias is likely to lead to an increased number of false-negative results, thereby leading to delayed treatments.