Prevalence learning and decision making in a visual search task: an equivalent ideal observer approach

Research studies have observed an influence of target prevalence on observer performance for visual search tasks. The goal of this work is to develop models for prevalence effects on visual search. In a recent study by Wolfe et. al, a large scale observer study was conducted to understand the effects of varying target prevalence on visual search. Particularly, a total of 12 observers were recruited to perform 1000 trials of simulated baggage search as target prevalence varied sinusoidally from high to low and back to high. We attempted to model observers’ behavior in prevalence learning and decision making. We modeled the observer as an equivalent ideal observer (EIO) with a prior belief of the signal prevalence. The use of EIO allows the application of ideal observer mathematics to characterize real observers’ performance reading real-life images. For every given new image, the observer updates the belief on prevalence and adjusts his/her decision threshold according to utility theory. The model results agree well with the experimental results from the Wolfe study. The proposed models allow theoretical insights into observer behavior in learning prevalence and adjusting their decision threshold.