Abstract
Perceptual classification, one canonical form of decision-making, entails assigning stimuli to discrete classes according to internal criteria. Accordingly, the standard formalisms of perceptual decision-making have incorporated both stimulus and criterion as necessary components, but granted them unequal representational status, stimulus a random variable and criterion a scalar variable. This representational inconsistency obscures identifying the origins of behavioral or neural variability in perceptual classification. Here, we redress this problem by presenting an alternative formalism in which criterion, as a latent random variable, plays causal roles in forming decision variable on equal footings with stimulus. By implementing this formalism into a Bayes-optimal algorithm, we could predict, simulate, and explain the key human classification behaviors with high fidelity and coherency. Further, by acquiring concurrent fMRI measurements from humans engaged in classification, we demonstrated an ensemble of brain activities that embodies the causal interactions between stimulus, criterion, and decision variable as the algorithm prescribes.