Magnitude estimation reveals Poisson-like noise underlying perception
Rodriguez-Arribas, C.; Lopez-Moliner, J.; Linares, D.
Show 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.
Matching journals
The top 5 journals account for 50% of the predicted probability mass.
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.