Imperfect Bayesian inference in visual perception
Fig 3
Results from the visual search conditions with unlimited display time.
(A) Left: AIC-based model comparison at the level of single subjects. Each column is a subject and each row is a model. The best model for each subject is indicated in dark blue (ΔAIC = 0). Right: Subject-averaged AIC values relative to the overall best model. The red dashed line indicates the ΔAIC≥10, which is interpreted as “no support”. (B) The subject data (black markers) are well accounted for by the “Imperfect Bayesian” and “Imperfect Max” models (black curves; the fits of both models are visually indistinguishable). Note that the distribution of d(s) (purple areas) becomes more concentrated around zero as the level of external uncertainty increases, due to the evidence generally being weaker in the tasks with more external uncertainty. (C) In all three conditions, the empirical d’ values (black) are lower than the values predicted by the Flawless Bayesian model (red). The average ratio between the d’ values is 0.834±0.017.