Albert-Ludwigs-Universität Freiburg, Germany.
Br J Math Stat Psychol. 2010 Nov;63(Pt 3):539-55. doi: 10.1348/000711009X477581. Epub 2009 Dec 23.
Diffusion model data analysis permits the disentangling of different processes underlying the effects of experimental manipulations. Estimates can be provided for the speed of information accumulation, for the amount of information used to draw conclusions, and for a decision bias. One parameter describes the duration of non-decisional processes including the duration of motor-response execution. In the default diffusion model, it is implicitly assumed that both responses are executed with the same speed. In some applications of the diffusion model, this assumption will be violated. This will lead to biased parameter estimates. Consequently, we suggest accounting explicitly for differences in the speed of response execution for both responses. Results from a simulation study illustrate that parameter estimates from the default model are biased if the speed of response execution differs between responses. A second simulation study shows that large trial numbers (N>1,000) are needed to detect whether differences in response-execution times are based on different execution times.
扩散模型数据分析允许分解实验操作效果背后的不同过程。可以提供信息积累速度、用于得出结论的信息量以及决策偏差的估计。一个参数描述了非决策过程的持续时间,包括运动反应执行的持续时间。在默认的扩散模型中,隐含地假设两个反应的执行速度相同。在扩散模型的某些应用中,这一假设将被违反。这将导致参数估计的偏差。因此,我们建议明确考虑两个反应的反应执行速度的差异。一项模拟研究的结果表明,如果两个反应的反应执行速度不同,则默认模型的参数估计会有偏差。第二项模拟研究表明,需要大量的试验次数(N>1000)来检测反应执行时间的差异是否基于不同的执行时间。