Fitting the psychometric function

A constrained generalized maximum likelihood routine for fitting psychometr ic functions is proposed, which determines optimum values for the complete parameter set-that is, threshold and slope-as well as for guessing and laps ing probability. The constraints are realized by Bayesian prior distributio ns for each of these parameters. The fit itself results from maximizing the posterior distribution of the parameter values by a multidimensional simpl ex method. We present results from extensive Monte Carlo simulations by whi ch we can approximate bias and variability of the estimated parameters of s imulated psychometric functions. Furthermore, we have tested the routine wi th data gathered in real sessions of psychophysical experimenting.