A novel behavioral paradigm reveals the nature of confidence computation in multi-alternative perceptual decision making.

A central goal of research in perceptual decision making is to determine the internal computations underlying choice and confidence in complex, multi-alternative tasks. However, revealing these computations requires knowledge of the internal representation upon which the computations operate. Unfortunately, it is unknown how traditional stimuli (e.g., Gabor patches and random dot motion) are represented internally, which calls into question the computations inferred when using such stimuli. Here we develop a new behavioral paradigm where subjects discriminate the dominant color in a cloud of differently colored dots. Critically, we show that the internal representation for these stimuli can be described with a simple, one-parameter equation and that a single free parameter can explain multi-alternative data for up to 12 different conditions. Further, we use this paradigm to test three popular theories: that confidence reflects (1) the probability of being correct, (2) only choice-congruent (i.e., positive) evidence, or (3) the evidence difference between the highest and the second-highest signal.The predictions of the first two theories were falsified in two experiments involving either six or 12 conditions with three choices each. We found that the data were best explained by a model where confidence is based on the difference of the two alternatives with the largest evidence. These results establish a new paradigm in which a single parameter can be used to determine the internal representation for an unlimited number of multi-alternative conditions and challenge two prominent theories of confidence computation.