Magnitude estimation reveals Poisson-like noise underlying perception

Abstract A fundamental goal of sensory neuroscience is to understand how stimuli map into perceptual experience. Insights into this process traditionally rely on measurements of discrimination sensitivity, but these cannot identify the internal noise distribution underlying perception because they conflate it with transducer gain. Here, using visual contrast, we show that rating variability from magnitude estimation provides access to this noise. The analysis of this variability alongside mean responses reveals an internal representation comprising a sigmoidal transducer and Poisson-like noise that quantitatively predicts discrimination sensitivity without free parameters. These predictions include the classic pedestal effect and Weber-like behavior, which now we can attribute to the transducer nonlinearities and signal-dependent noise, respectively. We empirically establish that a single stochastic representation underlies subjective ratings and objective discrimination.