Abstract
Responses to two redundant sensory signals are typically faster than responses to the individual component signals. This redundant signals effect (RSE) is extensively studied not only with an impressive variety of signals but also across different subject populations focusing on development, aging, and many clinical samples. Yet, a standardized methodology to analyse and interpret the RSE is not consistently employed. Moreover, an intriguing explanation of the effect, based on the so-called race model championed by Raab (1962), is typically not fully appreciated in its explanatory power. To facilitate RSE research, here, an analysis and modelling toolbox, called RSE-box, is introduced. It provides MATLAB functions that (1) perform basic analysis steps on response times (RTs), (2) investigate the RSE at the level of RT distributions, (3) fit the most recent race model by Otto and Mamassian (2012), and (4) simulate the RSE with synthetic data. The model functions are accompanied by parameter recovery simulations to test and validate the fitting procedures. An example of the simulation results is the demonstration that the analysis of group RT distributions can be biased. The RSE-box includes demonstration code and all functions are supported by help-documentation.