Likelihood-Based Parameter Estimation and Comparison of Dynamical Cognitive Models
Likelihood-Based Parameter Estimation and Comparison of Dynamical Cognitive Models
复制标题
DOI：
10.1037/rev0000068
复制
发表时间：
2017-07-01
影响因子：
5.4
通讯作者：
Engbert, Ralf
中科院分区：
文献类型：
--
作者：
Schuett, Heiko H.;Rothkegel, Lars O. M.;Engbert, Ralf
认知的动态模型在推动心理学理论和实验研究方面发挥着越来越重要的作用。因此，参数估计、模型分析和动力学模型比较至关重要。在本文中，我们提出了一种在包含按时间排序的实验数据的完全动态框架中进行模型分析的最大似然方法。我们的方法可以应用于动态模型来预测离散行为（例如，运动开始）；特别是，我们使用场景观看中扫视生成的动态模型作为我们方法的案例研究。对于该模型，可以通过数值模拟直接计算似然函数，从而可以更有效地进行包括贝叶斯推理在内的参数估计，以获得可靠的估计和相应的可信区间。对于个体观察者来说，使用分层模型进行推理甚至是可能的。此外，我们的似然方法可用于比较不同的模型。在我们的示例中，动态框架的性能优于非动态统计模型。此外，基于可能性的评估区分了模型变体，这对迄今为止使用的统计数据产生了难以区分的预测。我们的结果表明，似然方法是动态认知模型的一个有前途的框架。
Dynamical models of cognition play an increasingly important role in driving theoretical and experimental research in psychology. Therefore, parameter estimation, model analysis and comparison of dynamical models are of essential importance. In this article, we propose a maximum likelihood approach for model analysis in a fully dynamical framework that includes time-ordered experimental data. Our methods can be applied to dynamical models for the prediction of discrete behavior (e.g., movement onsets); in particular, we use a dynamical model of saccade generation in scene viewing as a case study for our approach. For this model, the likelihood function can be computed directly by numerical simulation, which enables more efficient parameter estimation including Bayesian inference to obtain reliable estimates and corresponding credible intervals. Using hierarchical models inference is even possible for individual observers. Furthermore, our likelihood approach can be used to compare different models. In our example, the dynamical framework is shown to outperform nondynamical statistical models. Additionally, the likelihood based evaluation differentiates model variants, which produced indistinguishable predictions on hitherto used statistics. Our results indicate that the likelihood approach is a promising framework for dynamical cognitive models.