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.