Human-like dissociations between confidence and accuracy in convolutional neural networks
Fig 6
Confidence-accuracy dissociations in a color discrimination task.
(A) The stimulus consisted of an array of eight colored circles. The task was to determine whether the mean color across the eight patches was more blue or red. In this example, the mean color is more blue than red. Energy manipulations involved joint changes to the intensity of color (the amount of “blueness” or “redness” of the patches as well the variance in color across the array. (B) The networks’ accuracy was matched across energy conditions, but confidence significantly increased with energy levels. (C) The separability between the stimulus categories as well as the variance of the evidence distributions increased with energy levels for all three networks. The panels on the top-left for each network show the average distance between the S1 and S2 evidence distributions across the 25 model instances. The panels on the bottom-left show the average standard deviation (SD) across the two distributions across all model instances. The panels on the right show the distribution of activations aggregated across all 25 network instances. *p<0.05; **p<0.01; ***p<0.001, ****p<0.0001; n.s., not significant.