Error-prone states in visual search

Poster Presentation: Monday, May 19, 2025, 8:30 am – 12:30 pm, Banyan Breezeway
Session: Visual Search: Attention, clinical

Jeremy Wolfe1, Jeunghwan Choi2; 1Brigham and Womens Hospital / Harvard Med, 2Graduate Program in Cognitive Science, Yonsei University

In visual search, why do people miss targets that are clearly visible? In a search for a T among Ls observers will reliably miss 5-10% of targets. When “retrospectively visible” targets are missed in tasks like breast cancer screening, radiologists can be sued. For TvsL search, Li et al (2024) found errors to be largely random with respect to the specific stimulus. That is, missing the target on one trial tended not to alter the probability of missing the same target in the same display a second time. Of course, you are more likely to miss hard-to-see targets, but if some class of stimuli produces, say, 20% errors, the specific 20% seems to occur randomly. What about the state of the observer? Certainly, errors fall with learning and rise with fatigue, but within a relative steady-state, does anything modulate the probability of error? Bruno et al (2024) have proposed that there is an electrophysiologically identifiable brain state (perhaps related to ‘mindwandering’) that is associated with being temporarily more error prone. Introspectively, it sometimes feels like errors occur in clumps. If this is the case in visual search, then the probability of another error should be elevated after an error. To assess this, we reanalyzed several visual search datasets. We computed the distribution of lags between successive errors. Random production of errors predicts a geometric distribution of lags. If one error marks entry into an error-prone state, then the likelihood of another error should rise after the error. Chi-sq tests reveal significant over-representation of errors shortly following other errors, especially for tasks without feedback. This effect appears to be smaller or non-existent for experiments where feedback may disrupt any error prone state. Of course, in the real world, reliable error feedback is often lacking.

Acknowledgements: Grant support from NSF 2146617 & NIH-NEI EY017001