A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.
A Bayesian approach to dynamical modeling of eye-movement control in reading of normal, mirrored, and scrambled texts.
复制标题
阅读正常文本、镜像文本和乱序文本时眼球运动控制动态建模的贝叶斯方法。
DOI：
10.31234/osf.io/nw2pb
复制
发表时间：
2019
影响因子：
5.4
通讯作者：
Ralf Engbert
中科院分区：
文献类型：
--
作者：
M. M. Rabe;Johan Chandra;A. Krügel;S. Seelig;S. Vasishth;Ralf Engbert
在阅读过程中的眼动控制中，已经开发了先进的面向过程的模型来再现行为数据。到目前为止，模型的复杂性和大量的模型参数阻碍了严格的统计推断和个体间差异的建模。在这里，我们提出了一个贝叶斯方法，这两个问题的一个代表性的计算模型的句子阅读（SWIFT; Engbert等人，心理学评论，112，2005，pp。777-813）。我们使用了来自36名受试者的实验数据，这些受试者以正常和四种操纵文本布局之一（例如，镜像和乱序字母）。SWIFT模型分别拟合受试者和实验条件，以研究受试者间的变异性。基于模型参数的后验分布，固定概率和持续时间可靠地恢复从模拟数据和再现保留的经验数据，在实验条件和受试者的水平。随后对阅读条件下的模型参数进行统计分析，生成条件之间可观察效应的模型驱动解释。（PsycInfo数据库记录（c）2021阿帕，保留所有权利）。
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).