Kollmeier B, Gilkey R H, Sieben U K
III. Physikalisches Institut, Universität Göttingen, West Germany.
J Acoust Soc Am. 1988 May;83(5):1852-62. doi: 10.1121/1.396521.
Data from a simple tone-in-noise simultaneous masking task were used to evaluate each of two common adaptive staircase rules (a "1 up 2 down" rule and a "1 up 3 down" rule) and the parameter estimation by sequential testing (PEST) technique in combination with each of two psychophysical procedures [a two-alternative forced-choice (2AFC) and a three-alternative forced-choice (3AFC) procedure]. These human data were compared to predictions generated by a mathematical model based on Markov theory. The model predicts that threshold estimates obtained with the adaptive techniques should be equal to those derived with equivalent "fixed signal level" techniques. However, the human data indicate that the adaptive techniques tend to yield lower thresholds. The model predicts that the standard error of a threshold estimate obtained from an adaptive technique will decrease and approach zero as the number of trials used to compute the estimate increases. The human data show greater variability than predicted and approach a nonzero value as the number of trials increases. The predictions of the model suggest that the commonly used combination of the 2AFC procedure and the 1 up 2 down rule is the least efficient method of estimating a threshold and that the 3AFC procedure in combination with the 1 up 3 down rule is the most efficient method. The human data are less consistent, but generally show the combination of the 2AFC procedure and the 1 up 2 down rule to be one of the least efficient methods. Possible explanations for the differences between the model's predictions and the human data, as well as suggestions for laboratory practice, are discussed.
来自一个简单的纯音掩蔽同步任务的数据被用于评估两种常见的自适应阶梯法则（“1上2下”法则和“1上3下”法则），以及通过顺序测试（PEST）技术结合两种心理物理学程序[二项迫选（2AFC）和三项迫选（3AFC）程序]进行的参数估计。这些人类数据与基于马尔可夫理论的数学模型生成的预测进行了比较。该模型预测，使用自适应技术获得的阈值估计应等于使用等效“固定信号水平”技术得出的阈值估计。然而，人类数据表明，自适应技术往往会产生较低的阈值。该模型预测，随着用于计算估计值的试验次数增加，从自适应技术获得的阈值估计的标准误差将减小并趋近于零。人类数据显示出比预测更大的变异性，并且随着试验次数的增加趋近于一个非零值。该模型的预测表明，常用的2AFC程序和“1上2下”法则的组合是估计阈值效率最低的方法，而3AFC程序与“1上3下”法则的组合是最有效的方法。讨论了模型预测与人类数据之间差异的可能解释以及对实验室实践的建议。
