A model of subjective report and objective discrimination as categorical decisions in a vast representational space.

Subliminal perception studies have shown that one can objectively discriminate a stimulus without subjectively perceiving it. We show how a minimalist framework based on Signal Detection Theory and Bayesian inference can account for this dissociation, by describing subjective and objective tasks with similar decision-theoretic mechanisms. Each of these tasks relies on distinct response classes, and therefore distinct priors and decision boundaries. As a result, they may reach different conclusions. By formalizing, within the same framework, forced-choice discrimination responses, subjective visibility reports and confidence ratings, we show that this decision model suffices to account for several classical characteristics of conscious and unconscious perception. Furthermore, the model provides a set of original predictions on the nonlinear profiles of discrimination performance obtained at various levels of visibility. We successfully test one such prediction in a novel experiment: when varying continuously the degree of perceptual ambiguity between two visual symbols presented at perceptual threshold, identification performance varies quasi-linearly when the stimulus is unseen and in an 'all-or-none' manner when it is seen. The present model highlights how conscious and non-conscious decisions may correspond to distinct categorizations of the same stimulus encoded by a high-dimensional neuronal population vector.