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        <title>A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior</title>
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    <abstract>Reading is a process that unfolds across space and time, alternating between fixations where a reader focuses on a specific point in space, and saccades where a reader rapidly shifts their focus to a new point. An ansatz of psycholinguistics is that modeling a reader’s fixations and saccades yields insight into their online sentence processing. However, standard approaches to such modeling rely on aggregated eye-tracking measurements and models that impose strong assumptions, ignoring much of the spatio-temporal dynamics that occur during reading. In this paper, we propose a more general probabilistic model of reading behavior, based on a marked spatio-temporal point process, that captures not only how long fixations last, but also where they land in space and when they take place in time. The saccades are modeled using a Hawkes process, which captures how each fixation excites the probability of a new fixation occurring near it in time and space. The duration time of fixation events is modeled as a function of fixation-specific predictors convolved across time, thus capturing spillover effects. Empirically, our Hawkes process model exhibits a better fit to human saccades than baselines. With respect to fixation durations, we observe that incorporating contextual surprisal as a predictor results in only a marginal improvement in the model’s predictive accuracy. This finding suggests that surprisal theory struggles to explain fine-grained eye movements.</abstract>
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%0 Conference Proceedings
%T A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior
%A Re, Francesco Ignazio
%A Opedal, Andreas
%A Manaiev, Glib
%A Giulianelli, Mario
%A Cotterell, Ryan
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F re-etal-2025-spatio
%X Reading is a process that unfolds across space and time, alternating between fixations where a reader focuses on a specific point in space, and saccades where a reader rapidly shifts their focus to a new point. An ansatz of psycholinguistics is that modeling a reader’s fixations and saccades yields insight into their online sentence processing. However, standard approaches to such modeling rely on aggregated eye-tracking measurements and models that impose strong assumptions, ignoring much of the spatio-temporal dynamics that occur during reading. In this paper, we propose a more general probabilistic model of reading behavior, based on a marked spatio-temporal point process, that captures not only how long fixations last, but also where they land in space and when they take place in time. The saccades are modeled using a Hawkes process, which captures how each fixation excites the probability of a new fixation occurring near it in time and space. The duration time of fixation events is modeled as a function of fixation-specific predictors convolved across time, thus capturing spillover effects. Empirically, our Hawkes process model exhibits a better fit to human saccades than baselines. With respect to fixation durations, we observe that incorporating contextual surprisal as a predictor results in only a marginal improvement in the model’s predictive accuracy. This finding suggests that surprisal theory struggles to explain fine-grained eye movements.
%R 10.18653/v1/2025.acl-long.1474
%U https://aclanthology.org/2025.acl-long.1474/
%U https://doi.org/10.18653/v1/2025.acl-long.1474
%P 30518-30538

Markdown (Informal)

[A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior](https://aclanthology.org/2025.acl-long.1474/) (Re et al., ACL 2025)

• A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior (Re et al., ACL 2025)

ACL

• Francesco Ignazio Re, Andreas Opedal, Glib Manaiev, Mario Giulianelli, and Ryan Cotterell. 2025. A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 30518–30538, Vienna, Austria. Association for Computational Linguistics.