Models of eye-movement control during reading, developed largely within psychology, usually focus on visual, attentional, lexical, 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 (Seelig et al., 2023) with key components of the Lewis and Vasishth sentence processing model (Lewis and Vasishth, 2005). 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 Sentence-Processing and Eye-Movement Activation-Coupled 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.

阅读过程中的眼动控制模型主要是在心理学中发展起来的，通常关注视觉、注意力、词汇和运动过程，但忽视词汇后语言处理；相比之下，主要在心理语言学范围内开发的句子理解过程模型通常只关注后词汇语言过程。我们提出了一个模型，通过整合眼动控制和句子处理来结合这两个研究线索。开发这样一个集成模型极具挑战性，并且计算要求很高，但这种集成是迈向阅读中自然语言理解完整数学模型的重要一步。我们将眼动控制的 SWIFT 模型 (Seelig et al., 2023) 与 Lewis 和 Vasishth 句子处理模型 (Lewis and Vasishth, 2005) 的关键组件结合起来。这种集成首次成为可能，部分原因是动态模型中参数识别的成功进展，这使我们能够研究各个模型参数的剖面对数似然。我们提出了一个完全实现的概念验证模型，展示了如何实现这种集成模型；我们的方法包括以马尔可夫链蒙特卡罗 (MCMC) 采样作为关键计算工具的贝叶斯模型推理。集成的句子处理和眼动激活耦合模型（SEAM）可以成功地再现由于阅读中基于相似性的干扰而出现的眼动模式。据我们所知，这是首次将眼动控制的完整过程模型与句子理解中的语言依存完成过程相结合。在未来的工作中，需要使用一组全面的基准数据来评估该概念验证模型。