A Bayesian prediction of four-look recognition performance from one-look data

The hypothesis that a human O’s (S’s) performance in a visual recognition task can be modelled by Bayes’ theorem was investigated. Two Ss were run for 40 experimental sessions each. Their task was to specify the direction of the gap of tachistoscopically presented Landolt rings (Cs). There were four possible gap directions, and two experimental conditions. In one condition, S responded after each stimulus presentation. In the other, a fixed-observation condition, Ss responded after four consecutive presentations of a C. Exposure durations were such that performance under both conditions was greater than chance, but less than unity. Predictions of four-look performance from one-look data were made. Overall hit rates were predicted closely. The entire pattern of each S’s four-look data was also predicted reasonably well. Further tests of the model are currently under way.