Hall J L
J Acoust Soc Am. 1981 Jun;69(6):1763-9. doi: 10.1121/1.385912.
Adaptive psychophysical procedures that have been described in the literature generally fall into one of two categories. (1) Simple procedures, such as UDTR and PEST, can be implemented without an on-line computer. The decision to change testing level is based on the outcome of the few most recent trials, and the final estimate of threshold is given by the final testing level or the average of a few testing levels. (2) "Maximum likelihood" methods require an on-line computer. A parametric form of the psychometric function is assumed, and after each trial a maximum-likelihood estimate of the parameters of the psychometric function is made on the basis of all preceding trials. This estimate is used to set the next testing level and to estimate threshold. We describe here a hybrid procedure, in which testing levels are determined by PEST and the final estimate of threshold is made by fitting an assumed psychometric function to all preceding trials. The PEST rules were tuned to yield results that were accurate and insensitive to errors in initial estimates of the psychometric function. These parameters differ from those that yield optimum results with classical PEST. Results of computer simulations and of experiments with human subjects are presented.
文献中描述的自适应心理物理学程序通常可分为两类。（1）简单程序，如UDTR和PEST，无需在线计算机即可实施。改变测试水平的决定基于最近几次试验的结果，阈值的最终估计由最终测试水平或几个测试水平的平均值给出。（2）“最大似然”方法需要在线计算机。假定心理测量函数的参数形式，每次试验后，根据之前所有试验对心理测量函数的参数进行最大似然估计。该估计用于设置下一个测试水平并估计阈值。我们在此描述一种混合程序，其中测试水平由PEST确定，阈值的最终估计通过将假定的心理测量函数拟合到之前所有试验来进行。对PEST规则进行了调整，以产生准确且对心理测量函数初始估计中的误差不敏感的结果。这些参数与经典PEST产生最佳结果的参数不同。文中呈现了计算机模拟结果和人体实验结果。
