Treutwein B, Strasburger H
Institut für Medizinische Psychologie, Ludwig-Maximilians-Universität, München, Germany.
Percept Psychophys. 1999 Jan;61(1):87-106. doi: 10.3758/bf03211951.
A constrained generalized maximum likelihood routine for fitting psychometric functions is proposed, which determines optimum values for the complete parameter set--that is, threshold and slope--as well as for guessing and lapsing probability. The constraints are realized by Bayesian prior distributions for each of these parameters. The fit itself results from maximizing the posterior distribution of the parameter values by a multidimensional simplex method. We present results from extensive Monte Carlo simulations by which we can approximate bias and variability of the estimated parameters of simulated psychometric functions. Furthermore, we have tested the routine with data gathered in real sessions of psychophysical experimenting.
提出了一种用于拟合心理测量函数的约束广义最大似然程序，该程序可确定完整参数集（即阈值和斜率）以及猜测和失误概率的最佳值。这些约束通过对每个参数的贝叶斯先验分布来实现。拟合本身是通过多维单纯形法最大化参数值的后验分布来实现的。我们展示了大量蒙特卡罗模拟的结果，通过这些结果我们可以近似模拟心理测量函数估计参数的偏差和变异性。此外，我们还使用在心理物理实验实际过程中收集的数据对该程序进行了测试。
