Fig. 3: Behavioral dissociations between decisions and confidence.
From: Natural statistics support a rational account of confidence biases
a Human and animal decision confidence displays a positive evidence (PE) bias: higher confidence (or lower opt-out rate) in the high vs. low PE conditions despite balanced signal-to-noise ratio and balanced decision accuracy. b The PE bias naturally emerges in performance-optimized neural networks across multiple datasets, architectures, and learning paradigms (two-sided paired t-tests; accuracy: MNIST, p = 0.42; CIFAR-10, p = 0.48; RL, p = 0.97; confidence: MNIST, p = 1.4 × 10−20; CIFAR-10, p = 4.3 × 10−50; RL, p = 4.4 × 10−20). See Supplementary Fig. S1 for confidence distributions in correct vs. incorrect trials. Note that stimulus parameters (contrast and noise) were set so as to target the threshold between chance performance (dotted black lines) and 100% accuracy, resulting in ~ 55% accuracy for 10-choice tasks and ~ 75% accuracy for two-choice tasks. The model achieved much higher accuracy when presented with noiseless images (96.3% ± 0.03 for MNIST, 88.1% ± 0.05 for CIFAR-10). c An alternative test for the PE bias, involving superimposed stimuli presented at different contrast levels, where the task is to indicate which stimulus is presented at a higher contrast. In the high positive evidence condition, there is both higher positive evidence (evidence in favor of the correct answer, 4 in this case), and higher negative evidence (evidence in favor of the incorrect answer, 6 in this case), than in the low positive evidence condition. Visual noise was also included in images, but is omitted here for clarity of visualization. d The model also shows this alternative formulation of the PE bias (two-sided paired t-tests; accuracy: MNIST, p = 0.8; CIFAR-10, p = 0.99; RL, p = 0.83; confidence: MNIST, p = 8.3 × 10−84; CIFAR-10, p = 2.8 × 10−48; RL, p = 2.8 × 10−10). e Adaptation of behavioral paradigm from Maniscalco et al.13, s1 is presented at an intermediate contrast, while the contrast of s2 is varied. f This produces a strong dissociation between type-1 sensitivity (d') and type-2 sensitivity (meta-d'): when participants respond s1, meta-d' decreases as d' increases (Behavior), a phenomenon which is captured by the neural network model (Model). Results in (b) and (d) reflect probability density over 100 trained networks, with mean accuracy/confidence in each condition represented by circular markers, and maxima/minima represented by the upper/lower line; results in (f) reflect mean d'/meta-d' over 100 trained networks ± the standard of the mean; ns indicates p > 0.05, ****p < 0.0001. Source data are provided as a Source Data file.
