Estimating discrimination performance in two-alternative forced choice tasks: routines for MATLAB and R

Abstract

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.