一种估计心理物理函数的极大似然程序：阈限、斜率和注意力的失误。
A maximum-likelihood procedure for estimating psychometric functions: thresholds, slopes, and lapses of attention.
机构信息
Department of Cognitive Sciences, University of California, Irvine, 3151 Social Science Plaza, Irvine, California 92687-5100, USA.
出版信息
J Acoust Soc Am. 2012 Aug;132(2):957-67. doi: 10.1121/1.4733540.
Green [J. Acoust. Soc. Am. 87, 2662-2674 (1990)] suggested an efficient, maximum-likelihood-based approach for adaptively estimating thresholds. Such procedures determine the signal strength on each trial by first identifying the most likely psychometric functions among the pre-proposed alternatives based on responses from previous trials, and then finding the signal strength at the "sweet point" on that most likely function. The sweet point is the point on the psychometric function that is associated with the minimum expected variance. Here, that procedure is extended to reduce poor estimates that result from lapses in attention. The sweet points for the threshold, slope, and lapse parameters of a transformed logistic psychometric function are derived. In addition, alternative stimulus placement algorithms are considered. The result is a relatively fast and robust estimation of a three-parameter psychometric function.
格林 [J. Acoust. Soc. Am. 87, 2662-2674 (1990)] 提出了一种高效、基于最大似然的自适应估计阈值的方法。这些程序通过首先根据前几次试验的反应，在预先提出的替代方案中确定最可能的心理物理函数，然后在该最可能的函数上找到“最佳点”，从而确定每次试验的信号强度。最佳点是与最小预期方差相关的心理物理函数上的点。在这里，该程序被扩展以减少由于注意力不集中而导致的估计不佳的情况。推导出变换逻辑心理物理函数的阈值、斜率和失误参数的最佳点。此外，还考虑了替代的刺激放置算法。其结果是对三参数心理物理函数的相对快速和稳健的估计。
Zotero 插件