Bayesian adaptive estimation of psychometric slope and threshold

Abstract

We introduce a new Bayesian adaptive method for acquisition of both threshold and slope of the psychometric function. The method updates posterior probabilities in the two-dimensional parameter space of psychometric functions and makes predictions based on the expected mean threshold and slope values. On each trial it sets the stimulus intensity that maximizes the expected information to be gained by completion of that trial. The method was evaluated in computer simulations and in a psychophysical experiment using the two-alternative forced-choice (2AFC) paradigm. Threshold estimation within 2 dB (23%) precision requires less than 30 trials for a typical 2AFC detection task. To get the slope estimate with the same precision takes about 300 trials.