Criterion learning for perceptual decisions under uncertainty


Signal detection theory posits that observers set a fixed criterion in making a forced-choice decision based on noisy sensory information. How is that criterion set if subjects have incomplete knowledge of the distributions corresponding to the two possible stimuli? We compare several models of criterion learning in experiments in which the observer’s criterion is either explicitly indicated on each trial, or is implicit and can only be inferred by the sequence of observer decisions.