Gondan Matthias
Department of Experimental Psychology, University of Regensburg, Regensburg, Germany.
J Exp Psychol Hum Percept Perform. 2009 Apr;35(2):575-9. doi: 10.1037/a0013620.
In speeded response tasks with redundant signals, parallel processing of the signals is tested by the race model inequality. This inequality states that given a race of two signals, the cumulative distribution of response times for redundant stimuli never exceeds the sum of the cumulative distributions of response times for the single-modality stimuli. It has been derived for synchronous stimuli and for stimuli with stimulus onset asynchrony (SOA). In most experiments with asynchronous stimuli, discrete SOA values are chosen and the race model inequality is separately tested for each SOA. Due to the high number of statistical tests, Type I and II errors are increased. Here a straightforward method is demonstrated to collapse these multiple tests into one test by summing the inequalities for the different SOAs. The power of the procedure is substantially increased by assigning specific weights to SOAs at which the violation of the race model prediction is expected to be large. In addition, the method enables data analysis for experiments in which stimuli are presented with SOA from a continuous distribution rather than in discrete steps.
在具有冗余信号的快速反应任务中，信号的并行处理通过竞争模型不等式进行检验。该不等式表明，在两个信号的竞争中，冗余刺激的反应时间累积分布永远不会超过单模态刺激的反应时间累积分布之和。它已被推导用于同步刺激和具有刺激起始异步性（SOA）的刺激。在大多数异步刺激实验中，选择离散的SOA值，并针对每个SOA分别检验竞争模型不等式。由于统计检验数量众多，I型和II型错误会增加。在此展示了一种直接的方法，通过对不同SOA的不等式求和，将这些多重检验合并为一次检验。通过为预期竞争模型预测会被违反的SOA赋予特定权重，该程序的功效会大幅提高。此外，该方法能够对刺激以连续分布而非离散步骤呈现SOA的实验进行数据分析。
