How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria
How many trials are required for parameter estimation in diffusion modeling? A comparison of different optimization criteria
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DOI：
10.3758/s13428-016-0740-2
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发表时间：
2017-04-01
影响因子：
5.4
通讯作者：
Nagler, Markus
中科院分区：
文献类型：
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作者：
Lerche, Veronika;Voss, Andreas;Nagler, Markus
扩散模型（Ratcliff，1978）使得识别和分离二元决策任务中反应背后的不同认知过程成为可能（例如，信息积累的速度与反应的保守程度）。由于信息的高度利用，这成为可能。参数估计不仅使用平均响应时间或错误率，而且还使用正确响应和错误响应的响应时间分布。在一系列模拟研究中，使用三种不同的优化标准（最大似然、Kolmogorov-Smirnov 和卡方），比较了复杂性（即自由参数数量）和试验数量（范围从 24 到 5,000）不同的模型的参数恢复的效率和鲁棒性，这些标准均在最新版本的 fast-dm 中实现（Voss、Voss 和 Lerche，2015）。结果表明，对于未污染的数据，最大似然法更为优越，但在存在快速污染物的情况下，Kolmogorov-Smirnov 的表现优于其他两种方法。对于大多数情况，基于卡方的参数估计导致的结果不如其他优化标准精确。将 fast-dm 方法的性能与 EZ 方法（Wagenmakers、van der Maas 和 Grasman，2007）以及贝叶斯实现（Wiecki、Sofer 和 Frank，2013）进行了比较。试验次数的建议源自不同复杂性模型的结果。有趣的是，在某些条件下，即使少量的试验（N < 100）也足以进行稳健的参数估计。
Diffusion models (Ratcliff, 1978) make it possible to identify and separate different cognitive processes underlying responses in binary decision tasks (e.g., the speed of information accumulation vs. the degree of response conservatism). This becomes possible because of the high degree of information utilization involved. Not only mean response times or error rates are used for the parameter estimation, but also the response time distributions of both correct and error responses. In a series of simulation studies, the efficiency and robustness of parameter recovery were compared for models differing in complexity (i.e., in numbers of free parameters) and trial numbers (ranging from 24 to 5,000) using three different optimization criteria (maximum likelihood, Kolmogorov-Smirnov, and chi-square) that are all implemented in the latest version of fast-dm (Voss, Voss, & Lerche, 2015). The results revealed that maximum likelihood is superior for uncontaminated data, but in the presence of fast contaminants, Kolmogorov-Smirnov outperforms the other two methods. For most conditions, chi-square-based parameter estimations lead to less precise results than the other optimization criteria. The performance of the fast-dm methods was compared to the EZ approach (Wagenmakers, van der Maas, & Grasman, 2007) and to a Bayesian implementation (Wiecki, Sofer, & Frank, 2013). Recommendations for trial numbers are derived from the results for models of different complexities. Interestingly, under certain conditions even small numbers of trials (N < 100) are sufficient for robust parameter estimation.