Turpin Andrew, McKendrick Allison M
Department of Computer Science and Software Engineering, University of Melbourne, Australia.
Vision Res. 2005 Nov;45(25-26):3277-89. doi: 10.1016/j.visres.2005.08.012. Epub 2005 Sep 22.
Determining confidence intervals on psychophysical thresholds is straight forward if the psychometric function is known. In clinical settings, however, there is only partial information about the psychometric function, hence confidence limits are usually derived from test-retest data collected from many subjects. In this paper, we introduce a computational technique for deriving confidence limits for an individual's endpoint threshold using data typically obtained in a clinical setting, rather than a database of test-retest performance. The technique uses probabilistic analysis of all possible response sequences in a test procedure. We then extend this procedure to allow for levels of typical uncertainty in data measurement.
如果心理测量函数已知,那么确定心理物理阈值的置信区间很简单。然而,在临床环境中,关于心理测量函数只有部分信息,因此置信限通常从许多受试者收集的重测数据中得出。在本文中,我们介绍一种计算技术,该技术使用通常在临床环境中获得的数据(而非重测表现的数据库)来推导个体终点阈值的置信限。该技术对测试过程中所有可能的反应序列进行概率分析。然后我们扩展此过程以考虑数据测量中典型的不确定水平。