Biasing Influence of ‘Mental Shortcuts’ on Diagnostic Decision-Making: Radiologists Can Overlook Breast Cancer in Mammograms When Prior Diagnostic Information Is Available

Abstract

When making decisions under uncertainty, people in all walks of life, including highly trained medical professionals, tend to resort to using ‘mental shortcuts’, or heuristics. Anchoring-and-adjustment (AAA) is a well-known heuristic in which subjects reach a judgment by starting from an initial internal judgment (‘anchored position’) based on available external information (‘anchoring information’) and adjusting it until they are satisfied. We studied the effects of the AAA heuristic during diagnostic decision-making in mammography. We provided practicing radiologists (N = 27 across two studies) a random number that we told them was the estimate of a previous radiologist of the probability that a mammogram they were about to see was positive for breast cancer. We then showed them the actual mammogram. We found that the radiologists’ own estimates of cancer in the mammogram reflected the random information they were provided and ignored the actual evidence in the mammogram. However, when the heuristic information was not provided, the same radiologists detected breast cancer in the same set of mammograms highly accurately, indicating that the effect was solely attributable to the availability of heuristic information. Thus, the effects of the AAA heuristic can sometimes be so strong as to override the actual clinical evidence in diagnostic tasks.

Heuristic Vetoing: Top-Down Influences of the Anchoring-and-Adjustment Heuristic Can Override the Bottom-Up Information in Visual Images

“…This has serious implications for real-world tasks involving visual search, such as airport baggage screening and medical image perception. Indeed, we have recently found a similar AAA ‘veto’ effect in practicing radiologists examining mammograms ( Branch et al, 2022 ).…”

“…Before initiating the present study, we carried out a priori power analyses to determine the subject recruitment target. To do this, we used the empirically observed fit of the data from a pilot study ( Branch et al, 2022 ) as the expected effect size, and calculated the total number of trials (pooled across all subjects). The results indicated that at least 47 trials (pooled across all subjects and repetitions) would be needed to achieve a statistical power of 0.90.…”