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

arXiv: https://arxiv.org/abs/2303.05221 · HTML: https://mlsanity.com/paper/2303.05221/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 97 references that are in the map.
- First posted on arXiv 9 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Understanding predictive information criteria for Bayesian models](https://mlsanity.com/paper/1307.5928/) (2013)
- [Toward a principled Bayesian workflow in cognitive science](https://mlsanity.com/paper/1904.12765/) (2019)
- [Likelihood-based Parameter Estimation and Comparison of Dynamical Cognitive Models](https://mlsanity.com/paper/1606.07309/) (2016)
- [Bayesian linear mixed models using Stan: A tutorial for psychologists, linguists, and cognitive scientists](https://mlsanity.com/paper/1506.06201/) (2015)
- [Bayesian parameter estimation for the SWIFT model of eye-movement control during reading](https://mlsanity.com/paper/1901.11110/) (2019)
- [A dynamical scan path model for task-dependence during scene viewing](https://mlsanity.com/paper/2112.11067/) (2021)
- [A computational investigation of sources of variability in sentence comprehension difficulty in aphasia](https://mlsanity.com/paper/1703.04677/) (2017)

## Built on by (most cited first, 3 of 3)

- [Integrating large language models and active inference to understand eye movements in reading and dyslexia](https://mlsanity.com/paper/2308.04941/) (2023)
- [Eye-Tracking-while-Reading: A Living Survey of Datasets with Open Library Support](https://mlsanity.com/paper/2602.19598/) (2026)
- [Eyettention II: A Dual-Sequence Architecture for Modeling Fixation Location, Within-Word Landing Position, and Fixation Duration in Reading](https://mlsanity.com/paper/2606.01964/) (2026)

## Abstract (arXiv)

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 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.