Psychometric function
From Wikipedia, the free encyclopedia

A psychometric function is an inferential psychometric model applied in detection and discrimination tasks. It models the relationship between a given feature of a physical stimulus, e.g. velocity, duration, brightness, weight etc., and forced-choice responses of a human or animal test subject. The psychometric function therefore is a specific application of the generalized linear model (GLM) to psychophysical data. The probability of response is related to a linear combination of predictors by means of a sigmoid link function (e.g. probit, logit, etc.).
Depending on the number of choices, the psychophysical experimental paradigms classify as simple forced choice (also known as yes-no task), two-alternative forced choice (2AFC), and n-alternative forced choice. The number of alternatives in the experiment determine the lower asymptote of the function.