Fig. 5: Learned confidence strategy best explained by ideal observer.

a Generalized version of positive evidence manipulation used to comprehensively evaluate both accuracy and confidence as a function of sensory evidence. Model was trained on classification of individual stimuli over the standard range of contrast and noise levels, then tested on images consisting of two superimposed stimuli belonging to classes s1 and s2, with independently varying contrast levels μ_s1 and μ_s2 (visual noise was also included in images presented to the model). b Decision accuracy resembled the BE rule, as expected given uniform sampling of sensory evidence space. c Confidence displayed a more complex pattern. d Confidence was best predicted by the latent ideal observer model, which outperformed regression models based on either the RCE or BE rules. Results reflect an average over 100 trained networks. Source data are provided as a Source Data file.