Ulrich Rolf, Schröter Hannes, Leuthold Hartmut, Birngruber Teresa
University of Tübingen, Germany.
University of Tübingen, Germany.
Cogn Psychol. 2015 May;78:148-74. doi: 10.1016/j.cogpsych.2015.02.005. Epub 2015 Apr 22.
An elaborated diffusion process model (a Diffusion Model for Conflict Tasks, DMC) is introduced that combines conceptual features of standard diffusion models with the notion of controlled and automatic processes. DMC can account for a variety of distributional properties of reaction time (RT) in conflict tasks (e.g., Eriksen flanker, Simon, Stroop). Specifically, DMC is compatible with all observed shapes of delta functions, including negative-going delta functions that are particularly challenging for the class of standard diffusion models. Basically, DMC assumes that the activations of controlled and automatic processes superimpose to trigger a response. Monte Carlo simulations demonstrate that the unfolding of automatic activation in time largely determines the shape of delta functions. Furthermore, the predictions of DMC are consistent with other phenomena observed in conflict tasks such as error rate patterns. In addition, DMC was successfully fitted to experimental data of the standard Eriksen flanker and the Simon task. Thus, the present paper reconciles the prominent and successful class of diffusion models with the empirical finding of negative-going delta functions.
本文介绍了一种精细的扩散过程模型(冲突任务扩散模型,DMC),该模型将标准扩散模型的概念特征与受控和自动过程的概念相结合。DMC可以解释冲突任务中反应时间(RT)的各种分布特性(例如,埃里克森侧翼任务、西蒙任务、斯特鲁普任务)。具体而言,DMC与所有观察到的增量函数形状兼容,包括对标准扩散模型类别特别具有挑战性的负向增量函数。基本上,DMC假设受控和自动过程的激活叠加以触发反应。蒙特卡罗模拟表明,自动激活随时间的展开在很大程度上决定了增量函数的形状。此外,DMC的预测与冲突任务中观察到的其他现象(如错误率模式)一致。此外,DMC已成功拟合到标准埃里克森侧翼任务和西蒙任务的实验数据。因此,本文将著名且成功的扩散模型类别与负向增量函数的实证发现进行了协调。