Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, and motor processes but neglect post-lexical language processing; by contrast, models of sentence comprehension processes, developed largely within psycholinguistics, generally focus only on post-lexical language processes. We present a model that combines these two research threads, by integrating eye-movement control and sentence processing. Developing such an integrated model is extremely challenging and computationally demanding, but such an integration is an important step toward complete mathematical models of natural language comprehension in reading. We combine the SWIFT model of eye-movement control (Engbert et al., Psychological Review, 112, 2005, pp. 777-813) with key components of the Lewis and Vasishth sentence processing model (Lewis and Vasishth, Cognitive Science, 29, 2005, pp. 375-419). This integration becomes possible, for the first time, due in part to recent advances in successful parameter identification in dynamical models, which allows us to investigate profile log-likelihoods for individual model parameters. We present a fully implemented proof-of-concept model demonstrating how such an integrated model can be achieved; our approach includes Bayesian model inference with Markov Chain Monte Carlo (MCMC) sampling as a key computational tool. The integrated model, SEAM, can successfully reproduce eye movement patterns that arise due to similarity-based interference in reading. To our knowledge, this is the first-ever integration of a complete process model of eye-movement control with linguistic dependency completion processes in sentence comprehension. In future work, this proof of concept model will need to be evaluated using a comprehensive set of benchmark data.

翻译：主要发展自心理学的阅读眼动控制模型通常关注视觉、注意和运动过程，但忽略词汇后语言加工；而主要发展自心理语言学的句子理解加工模型一般只关注词汇后语言过程。我们提出一个整合这两条研究线索的模型，通过融合眼动控制与句子加工来实现。构建这样的集成模型极具挑战性且计算需求巨大，但这种整合是向建立完整的自然语言阅读理解数学模型迈出的重要一步。我们将SWIFT眼动控制模型（Engbert等，《Psychological Review》，112卷，2005年，第777-813页）与Lewis和Vasishth句子加工模型（Lewis和Vasishth，《Cognitive Science》，29卷，2005年，第375-419页）的关键组件相结合。这种整合首次成为可能，部分得益于动力学模型参数成功识别的最新进展，使我们能够研究单个模型参数的剖面对数似然。我们呈现了一个完整实现的可行性示范模型，展示了如何实现这种集成；我们的方法包括以马尔可夫链蒙特卡洛（MCMC）抽样作为关键计算工具的贝叶斯模型推断。该集成模型SEAM能成功再现阅读中因基于相似性的干扰而产生的眼动模式。据我们所知，这是首次将完整的眼动控制过程模型与句子理解中的语言依存完成过程进行整合。未来工作中，这一可行性示范模型将需要使用全面的基准数据集进行验证。