Abstract Process-oriented theories of cognition must be evaluated against time-ordered observations. Here we present a representative example for data assimilation of the SWIFT model, a dynamical model of the control of fixation positions and fixation durations during natural reading of single sentences. First, we develop and test an approximate likelihood function of the model, which is a combination of a spatial, pseudo-marginal likelihood and a temporal likelihood obtained by probability density approximation Second, we implement a Bayesian approach to parameter inference using an adaptive Markov chain Monte Carlo procedure. Our results indicate that model parameters can be estimated reliably for individual subjects. We conclude that approximative Bayesian inference represents a considerable step forward for computational models of eye-movement control, where modeling of individual data on the basis of process-based dynamic models has not been possible so far.

中文翻译：

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阅读时眼动控制SWIFT模型的贝叶斯参数估计

摘要 面向过程的认知理论必须根据时间顺序的观察进行评估。在这里，我们提出了 SWIFT 模型数据同化的代表性示例，这是一种在自然阅读单个句子时控制注视位置和注视持续时间的动力学模型。首先，我们开发并测试模型的近似似然函数，它是空间、伪边际似然和通过概率密度近似获得的时间似然的组合第二，我们使用自适应马尔可夫链实现参数推断的贝叶斯方法蒙特卡罗程序。我们的结果表明，可以可靠地估计单个受试者的模型参数。