Quest: a bayesian adaptive psychometric method,” Percept Psychophys 33

An adaptive psychometric procedure that places each trial at the current most probable Baye& ian estimate of threshold is described. The procedure takes advantage of the common finding that the human psychometric function is invariant in form when expressed as a function of log intensity. The procedure is simple, fast, ana efficient, and may be easily implemented on any computer. A psychometric function describes the relation be-tween some physical measure of a stimulus and the probability of a particular psychophysical response. The physical measure is usually stimulus strength, and the response is "yes " (in a yes/no experiment) or "correct " (in a forced-choice experiment). More generally, there are several possible responses, each with its own psychometric function (e.g., in a rating scale experiment). Psychometric procedures are ways of testing the observer so as to gain information about the psychometric function. The advantages of adap-tive procedures, which make use of previous responses to guide further testing, have been discussed by nu-merous authors (Cocnsweet, 1962; Levitt, 1971; Taylor & Creelman, 1967; Wetherill & Levitt, 1965). Because trials are more effective when · they are judiciously placed, an adaptive procedure will be more efficient the more it makes intelligent use of available information. This information is of two sorts: that derived from previous trials (data), and that drawn from the memory of the experimenter, published reports, and so on (prior knowledge). Prior knowledge may be further divided into information about the shape of the psychometric function and knowledge about threshold in the particular condi-tion under study. Several recent methods make effi-cient use of the data and of knowledge about the shape of the psychometric function by calculating after each trial a maximum likelihood estimate o