Psychometric function

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.).

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.). The second way of plotting psychometric functions is often preferable, as it is more easily amenable to principled quantitative analysis using tools such as probit analysis (fitting of cumulative Gaussian distributions). However, it also has important drawbacks. First, the threshold estimation is based only on p(yes), namely on "Hit" in Signal Detection Theory terminology. Second, and consequently, it is not bias free or criterion free. Third, the threshold is identified with the p(yes) = .5, which is just a conventional and arbitrary choice.

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