Paradox Resolved: Stop Signal Race Model With Negative Dependence
Paradox Resolved: Stop Signal Race Model With Negative Dependence
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DOI：
10.1037/rev0000127
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发表时间：
2018-11-01
影响因子：
5.4
通讯作者：
Diederich, Adele
中科院分区：
文献类型：
--
作者：
Colonius, Hans;Diederich, Adele
主动抑制反应的能力是认知控制的一个重要例子。停止信号范式是研究反应抑制的常用工具。参与者执行响应时间任务（go任务），偶尔，go刺激在可变延迟后跟随停止信号，指示受试者保留他们的响应（停止任务）。建模的主要兴趣是估计不可观察的停止信号处理时间，即停止过程的隐蔽延迟作为反应抑制机制的表征。在独立竞赛模型中，停止信号任务被表示为随机独立的go和stop进程之间的竞赛。在不对处理时间做出任何具体分布假设的情况下，该模型允许估计取消响应的平均时间。然而，对反命令扫视眼球运动的神经生理学研究表明，走和停过程的神经相关物由相互作用的注视转移和注视保持神经元网络组成。这对在行为和神经发现之间建立联系提出了一个重大挑战。在这里，我们提出了一个依赖的比赛模型，假设完美的负随机依赖之间的去和停止激活。该模型与交互过程的概念是一致的，同时保留了无分布独立种族模型的简单性和优雅性。对于均值数据，非独立模型的预测与独立模型的预测保持一致。这个明显的悖论的解决方案推进了对反应抑制机制的理解，并为模拟更复杂的情况铺平了道路。
The ability to inhibit our responses voluntarily is an important case of cognitive control. The stop-signal paradigm is a popular tool to study response inhibition. Participants perform a response time task (go task), and occasionally, the go stimulus is followed by a stop signal after a variable delay, indicating subjects to withhold their response (stop task). The main interest of modeling is in estimating the unobservable stop-signal processing time, that is, the covert latency of the stopping process as a characterization of the response inhibition mechanism. In the independent race model, the stop-signal task is represented as a race between stochastically independent go and stop processes. Without making any specific distributional assumptions about the processing times, the model allows estimating the mean time to cancel a response. Neurophysiological studies on countermanding saccadic eye movements, however, have shown that the neural correlates of go and stop processes consist of networks of mutually interacting gaze-shifting and gaze-holding neurons. This poses a major challenge in formulating linking propositions between the behavioral and neural findings. Here we propose a dependent race model that postulates perfect negative stochastic dependence between go and stop activations. The model is consistent with the concept of interacting processes while retaining the simplicity and elegance of the distribution-free independent race model. For mean data, the dependent model's predictions remain identical to those of the independent model. The resolution of this apparent paradox advances the understanding of mechanisms of response inhibition and paves the way for modeling more complex situations.