Estimating discrimination performance in two-alternative forced choice tasks: Routines for MATLAB and R
Estimating discrimination performance in two-alternative forced choice tasks: Routines for MATLAB and R
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DOI：
10.3758/s13428-012-0207-z
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发表时间：
2012-12-01
影响因子：
5.4
通讯作者：
Ulrich, Rolf
中科院分区：
文献类型：
--
作者：
Bausenhart, Karin M.;Dyjas, Oliver;Ulrich, Rolf
Ulrich和Vorberg（Attention，Perception，& Psychophysics 71：1219-1227，2009）介绍了一种新的方法，用于估计两种选择强迫选择（2AFC）任务中的辨别性能。这种方法避免了传统方法中在估计差异阈值（DL）时忽略标准品顺序和比较的固有缺陷。本文提供了MATLAB和R例程，实现了估计DL的新过程。这些例程还允许考虑处理失败，如失误或手指错误，并可以应用于实验设计中的标准和比较不同，只有沿着的任务相关的维度，以及设计中的刺激不同，在一个以上的维度。此外，还进行了蒙特卡罗模拟，以检查我们的例程的质量。
Ulrich and Vorberg (Attention, Perception, & Psychophysics 71: 1219-1227, 2009) introduced a novel approach for estimating discrimination performance in two-alternative forced choice (2AFC) tasks. This approach avoids pitfalls that are inherent when the order of the standard and the comparison is neglected in estimating the difference limen (DL), as in traditional approaches. The present article provides MATLAB and R routines that implement this novel procedure for estimating DLs. These routines also allow to account for processing failures such as lapses or finger errors and can be applied to experimental designs in which the standard and comparison differ only along the task-relevant dimension, as well as to designs in which the stimuli differ in more than one dimension. In addition, Monte Carlo simulations were conducted to check the quality of our routines.