López Francisco M, Pomi Andrés
Frankfurt Institute for Advanced Studies, Ruth-Moufang-Str. 1, 60438 Frankfurt, Germany.
Facultad de Ciencias, Universidad de la República, Iguá 4225, 11400 Montevideo, Uruguay.
Cogn Neurodyn. 2024 Aug;18(4):1507-1524. doi: 10.1007/s11571-023-09990-8. Epub 2023 Jul 11.
Laboratory data from conflict tasks, e.g. Simon and Eriksen tasks, reveal differences in response time distributions under different experimental conditions. Only recently have evidence accumulation models successfully reproduced these results, in particular the challenging delta plots with negative slopes. They accomplish this with explicit temporal dependencies in their structure or activation functions. In this work, we introduce an alternative approach to the modeling of decision-making in conflict tasks exclusively based on inhibitory dynamics within a dual-route architecture. We consider simultaneous automatic and controlled drift diffusion processes, with the latter inhibiting the former. Our proposed Dual-Route Evidence Accumulation Model (DREAM) achieves equivalent performance to previous works in fitting experimental response time distributions despite having no time-dependent functions. The model can reproduce conditional accuracy functions and delta plots with positive and negative slopes. The implications of these results, including an interpretation of the parameters and potential links to perceptual representations, are discussed. We provide Python code to fit DREAM to experimental data.
The online version contains supplementary material available at 10.1007/s11571-023-09990-8.
来自冲突任务(例如西蒙任务和埃里克森任务)的实验室数据揭示了不同实验条件下反应时间分布的差异。直到最近,证据积累模型才成功再现了这些结果,尤其是具有负斜率的具有挑战性的增量图。它们通过其结构或激活函数中的显式时间依赖性来实现这一点。在这项工作中,我们引入了一种替代方法,专门基于双路径架构内的抑制动力学对冲突任务中的决策进行建模。我们考虑同时进行的自动和受控漂移扩散过程,后者抑制前者。我们提出的双路径证据积累模型(DREAM)在拟合实验反应时间分布方面实现了与先前工作相当的性能,尽管没有时间依赖函数。该模型可以再现具有正斜率和负斜率的条件准确性函数和增量图。讨论了这些结果的含义,包括对参数的解释以及与感知表征的潜在联系。我们提供了将DREAM拟合到实验数据的Python代码。
在线版本包含可在10.1007/s11571-023-09990-8获取的补充材料。