In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between-subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

中文翻译：

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在阅读正常文本、镜像文本和加扰文本时动态建模眼动控制的贝叶斯方法。

在阅读过程中的眼动控制方面，已经开发出先进的面向过程的模型来重现行为数据。到目前为止，模型的复杂性和大量的模型参数阻碍了对个体差异的严格统计推断和建模。在这里，我们针对一个具有代表性的句子阅读计算模型提出了针对这两个问题的贝叶斯方法（SWIFT；Engbert 等，Psychological Review，112, 2005，pp. 777-813）。我们使用了来自 36 名以正常文本和四种操纵文本布局之一（例如，镜像和加扰字母）阅读文本的受试者的实验数据。SWIFT 模型分别适用于受试者和实验条件，以研究受试者之间的变异性。基于模型参数的后验分布，固定概率和持续时间可以从模拟数据中可靠地恢复，并在实验条件和受试者级别为保留的经验数据进行复制。随后对跨阅读条件的模型参数进行统计分析，生成了对条件之间可观察到的影响的模型驱动解释。（PsycInfo 数据库记录 (c) 2021 APA，保留所有权利）。