论文标题
停止信号反应时间分布的贝叶斯混合物建模
A Bayesian Mixture Modelling of Stop Signal Reaction Time Distributions
论文作者
论文摘要
Hans Colonius（1990）用非参数方法对停止信号任务（SST）中单个停止信号反应时间（SSRT）的分布作为对不可观察的停止过程潜伏期的测量，并用Eric-Jan Wagenmakers and Culaeagues（2012年）对贝叶斯参数方法进行了建模。这些方法在SST试验中对先前的试验类型（GO/Stop）对SSRT分布估计的同等影响，而无需解决违反假设的情况。这项研究通过考虑用于SSRT分布的两态混合模型来介绍所需的模型。然后，它比较了前高斯分布形式下的贝叶斯参数单SSRT和混合物SSRT分布在个体和种群水平的通常随机顺序。它表明，与单个SSRT分布相比，混合物SSRT分布更加多样化，更积极地倾斜，更leptokurtic，并且按随机顺序更大。结果中差异的大小也取决于混合物SSRT分布中的权重的选择。这项研究证实，混合SSRT指数作为常数或分布明显大于相关顺序的单个SSRT对应物。这为SSRT估计提供了重要的改进。
The distribution of single Stop Signal Reaction Times (SSRT) in the stop signal task (SST) as a measurement of the latency of the unobservable stopping process has been modeled with a nonparametric method by Hans Colonius (1990) and with a Bayesian parametric method by Eric-Jan Wagenmakers and colleagues (2012). These methods assume equal impact of the preceding trial type (go/stop) in the SST trials on the SSRT distributional estimation without addressing the case of the violated assumption. This study presents the required model by considering two-state mixture model for the SSRT distribution. It then compares the Bayesian parametric single SSRT and mixture SSRT distributions in the usual stochastic order at the individual and the population level under the ex-Gaussian distributional format. It shows that compared to a single SSRT distribution, the mixture SSRT distribution is more diverse, more positively skewed, more leptokurtic, and larger in stochastic order. The size of the disparities in the results also depends on the choice of weights in the mixture SSRT distribution. This study confirms that mixture SSRT indices as a constant or distribution are significantly larger than their single SSRT counterparts in the related order. This offers a vital improvement in the SSRT estimations.
