Challenging the Bayesian confidence hypothesis in perceptual decision-making

The Bayesian confidence hypothesis (BCH), which postulates that confidence reflects the posterior probability that a decision is correct, is currently the most prominent theory of confidence. Although several recent studies have found evidence against it in the context of relatively complex tasks, BCH remains dominant for simpler tasks. The major alternative to BCH is the confidence in raw evidence space (CRES) hypothesis, according to which confidence is based directly on the raw sensory evidence without explicit probability computations. Here, we tested these competing hypotheses in the context of perceptual tasks that are assumed to induce Gaussian evidence distributions. We show that providing information about task difficulty gives rise to a basic behavioral signature that distinguishes BCH from CRES models even for simple 2-choice tasks. We examined this signature in three experiments and found that all experiments exhibited behavioral signatures in line with CRES computations but contrary to BCH ones. We further performed an extensive comparison of 16 models that implemented either BCH or CRES confidence computations and systematically differed in their auxiliary assumptions. These model comparisons provided overwhelming support for the CRES models over their BCH counterparts across all model variants and across all three experiments. These observations challenge BCH and instead suggest that humans may make confidence judgments by placing criteria directly in the space of the sensory evidence.