Modeling Individual Differences in the Go/No-go Task with a Diffusion Model.
Modeling Individual Differences in the Go/No-go Task with a Diffusion Model.
复制标题
DOI：
10.1037/dec0000065
复制
发表时间：
2018-01
期刊：
影响因子：
--
通讯作者：
McKoon G
中科院分区：
文献类型：
--
作者：
Ratcliff R;Huang-Pollock C;McKoon G
进行/不进行任务是一种有两种选择的任务，但受试者只对其中一种做出反应，等待另一个选择的暂停。这项任务在心理学领域有着悠久的历史，并且在临床/神经心理学领域具有现代应用。在本文中，我们将扩散模型拟合到实验数据和模拟数据。该模型与二项选择模型相同，并假设有两个决策边界，并且在其中一个边界处终止会产生响应，在另一个边界处终止时，受试者等待试验结束。在之前的建模中，同时拟合二选一数据和通过/不通过数据，并且仅拟合组数据。这里的模型仅适用于个别受试者的通过/不通过数据。这可以分析个体差异，这对于临床应用很重要。首先，我们将标准二项选择模型拟合到二项选择数据，并将通过/不通过模型拟合到来自其中一项选择的 RT 和两项选择数据的准确性。模型之间的参数值相似并且具有高度相关性。通过/不通过模型也适用于来自与二选一任务相同主题的通过/不通过版本任务的数据。对实践中获得的参数值范围进行的模拟研究表明，两种选择模型和通过/不通过模型之间的参数恢复相似。结果表明，具有隐式（无响应）边界的扩散模型可以以几乎与将二选模型拟合二选数据相同的精度来拟合数据。
The go/no-go task is one in which there are two choices, but the subject responds only to one of them, waiting out a time-out for the other choice. The task has a long history in psychology and modern applications in the clinical/neuropsychological domain. In this article we fit a diffusion model to both experimental and simulated data. The model is the same as the two-choice model and assumes that there are two decision boundaries and termination at one of them produces a response and at the other, the subject waits out the trial. In prior modeling, both two-choice and go/no-go data were fit simultaneously and only group data were fit. Here the model is fit to just go/no-go data for individual subjects. This allows analyses of individual differences which is important for clinical applications. First, we fit the standard two-choice model to two-choice data and fit the go/no-go model to RTs from one of the choices and accuracy from the two-choice data. Parameter values were similar between the models and had high correlations. The go/no-go model was also fit to data from a go/no-go version of the task with the same subjects as the two-choice task. A simulation study with ranges of parameter values that are obtained in practice showed similar parameter recovery between the two-choice and go/no-go models. Results show that a diffusion model with an implicit (no response) boundary can be fit to data with almost the same accuracy as fitting the two-choice model to two-choice data.