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Models of eye-movement control during reading, developed largely withinpsychology, usually focus on visual, attentional, lexical, and motor processesbut neglect post-lexical language processing; by contrast, models of sentencecomprehension processes, developed largely within psycholinguistics, generallyfocus only on post-lexical language processes. We present a model that combinesthese two research threads, by integrating eye-movement control and sentenceprocessing. Developing such an integrated model is extremely challenging andcomputationally demanding, but such an integration is an important step towardcomplete mathematical models of natural language comprehension in reading. Wecombine the SWIFT model of eye-movement control (Seelig et al., 2020,doi:10.1016/j.jmp.2019.102313) with key components of the Lewis and Vasishthsentence processing model (Lewis & Vasishth, 2005,doi:10.1207/s15516709cog0000_25). This integration becomes possible, for thefirst time, due in part to recent advances in successful parameteridentification in dynamical models, which allows us to investigate profilelog-likelihoods for individual model parameters. We present a fully implementedproof-of-concept model demonstrating how such an integrated model can beachieved; our approach includes Bayesian model inference with Markov ChainMonte Carlo (MCMC) sampling as a key computational tool. The integratedSentence-Processing and Eye-Movement Activation-Coupled Model (SEAM) cansuccessfully reproduce eye movement patterns that arise due to similarity-basedinterference in reading. To our knowledge, this is the first-ever integrationof a complete process model of eye-movement control with linguistic dependencycompletion processes in sentence comprehension. In future work, this proof ofconcept model will need to be evaluated using a comprehensive set of benchmarkdata.

SEAM: An Integrated Activation-Coupled Model of Sentence Processing and Eye Movements in Reading