A simple two-stage model predicts response time distributions
A simple two-stage model predicts response time distributions
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DOI：
10.1113/jphysiol.2009.173955
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发表时间：
2009-08-15
影响因子：
5.5
通讯作者：
Anderson, A. J.
中科院分区：
文献类型：
--
作者：
Carpenter, R. H. S.;Reddi, B. A. J.;Anderson, A. J.
反应时间背后的神经机制之前曾被用两种不同的方式建模。当很难检测到刺激时，反应时间倾向于遵循随机行走模型，该模型集成了噪声感觉信号。但调查先验概率和响应紧迫度等更高水平因素的影响的研究通常使用高度可探测的目标，然后响应时间通常对应于线性上升到阈值的机制。在这里，我们展示了一个将两种类型的元素串联在一起的模型--一个集成噪声传入信号的检测器，然后是执行判决的线性上升到阈值--不仅成功地预测了平均响应时间，而且更严格地预测了在广泛的刺激可检测性范围内观察到的这些时间的分布和判决错误率。通过调和以前似乎相互矛盾的理论，我们现在更接近于对反应时间和作为其基础的决策过程的完整描述。
The neural mechanisms underlying reaction times have previously been modelled in two distinct ways. When stimuli are hard to detect, response time tends to follow a random-walk model that integrates noisy sensory signals. But studies investigating the influence of higher-level factors such as prior probability and response urgency typically use highly detectable targets, and response times then usually correspond to a linear rise-to-threshold mechanism. Here we show that a model incorporating both types of element in series - a detector integrating noisy afferent signals, followed by a linear rise-to-threshold performing decision - successfully predicts not only mean response times but, much more stringently, the observed distribution of these times and the rate of decision errors over a wide range of stimulus detectability. By reconciling what previously may have seemed to be conflicting theories, we are now closer to having a complete description of reaction time and the decision processes that underlie it.