A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior

• URL: http://arxiv.org/abs/2506.19999v1
• Date: Tue, 24 Jun 2025 20:39:21 GMT
• Title: A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior
• Authors: Francesco Ignazio Re, Andreas Opedal, Glib Manaiev, Mario Giulianelli, Ryan Cotterell,
• Abstract summary: Ansatz of psycholinguistics is that modeling a reader's fixations andcades yields insight into their online sentence processing.<n>Standard approaches to such modeling rely on eye-tracking measurements aggregated and models that impose strong assumptions.<n>In this paper, we propose a more general probabilistic model of reading behavior, based on a marked sac-temporal point process.<n>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.
• Score: 47.47269936037604
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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