A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916810645831680 |
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| author | Re, Francesco Ignazio Opedal, Andreas Manaiev, Glib Giulianelli, Mario Cotterell, Ryan |
| author_facet | Re, Francesco Ignazio Opedal, Andreas Manaiev, Glib Giulianelli, Mario Cotterell, Ryan |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2506_19999 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior Re, Francesco Ignazio Opedal, Andreas Manaiev, Glib Giulianelli, Mario Cotterell, Ryan Machine Learning Computation and Language Neurons and Cognition 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. |
| title | A Spatio-Temporal Point Process for Fine-Grained Modeling of Reading Behavior |
| topic | Machine Learning Computation and Language Neurons and Cognition |
| url | https://arxiv.org/abs/2506.19999 |