Dynamical models are crucial for developing process-oriented, quantitative theories in cognition and behavior. Due to the impressive progress in cognitive theory, domain-specific dynamical models are complex, which typically creates challenges in statistical inference. Mathematical models of eye-movement control might be looked upon as a representative case study. In this tutorial, we introduce and analyze the SWIFT model (Engbert et al., 2002; Engbert et al., 2005), a dynamical modeling framework for eye-movement control in reading that was developed to explain all types of saccades observed in experiments from an activation-based approach. We provide an introduction to dynamical modeling, which explains the basic concepts of SWIFT and its statistical inference. We discuss the likelihood function of a simplified version of the SWIFT model as a key foundation for Bayesian parameter estimation (Rabe et al., 2021; Seelig et al., 2019). In posterior predictive checks, we demonstrate that the simplified model can reproduce interindividual differences via parameter variation. All computations in this tutorial are implemented in the -Language for Statistical Computing and are made publicly available. We expect that the tutorial might be helpful for advancing dynamical models in other areas of cognitive science.

中文翻译：

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阅读期间眼球运动控制动态建模的贝叶斯推理教程

动力学模型对于发展认知和行为中面向过程的定量理论至关重要。由于认知理论取得了令人瞩目的进展，特定领域的动力学模型非常复杂，这通常会给统计推断带来挑战。眼动控制的数学模型可以被视为代表性案例研究。在本教程中，我们介绍并分析 SWIFT 模型（Engbert 等人，2002 年；Engbert 等人，2005 年），这是一种用于阅读中眼动控制的动态建模框架，旨在解释实验中观察到的所有类型的眼跳来自基于激活的方法。我们介绍了动态建模，解释了 SWIFT 的基本概念及其统计推断。我们讨论了 SWIFT 模型简化版本的似然函数，作为贝叶斯参数估计的关键基础（Rabe 等人，2021 年；Seelig 等人，2019 年）。在后验预测检查中，我们证明简化模型可以通过参数变化再现个体间差异。本教程中的所有计算均以统计计算语言实现，并且公开可用。我们希望本教程可能有助于推进认知科学其他领域的动力学模型。