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., 2020,
doi:10.1016/j.jmp.2019.102313) with key components of the Lewis and Vasishth
sentence processing model (Lewis & Vasishth, 2005,
doi:10.1207/s15516709cog0000_25). 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