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

arXiv: https://arxiv.org/abs/2506.19999 · HTML: https://mlsanity.com/paper/2506.19999/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 78 references that are in the map.
- First posted on arXiv 24 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [mGPT: Few-Shot Learners Go Multilingual](https://mlsanity.com/paper/2204.07580/) (2022)
- [On the Predictive Power of Neural Language Models for Human Real-Time Comprehension Behavior](https://mlsanity.com/paper/2006.01912/) (2020)
- [Why Does Surprisal From Larger Transformer-Based Language Models Provide a Poorer Fit to Human Reading Times?](https://mlsanity.com/paper/2212.12131/) (2022)
- [Testing the Predictions of Surprisal Theory in 11 Languages](https://mlsanity.com/paper/2307.03667/) (2023)
- [Lower Perplexity is Not Always Human-Like](https://mlsanity.com/paper/2106.01229/) (2021)
- [Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention](https://mlsanity.com/paper/2010.07891/) (2020)
- [A Review of Self-Exciting Spatio-Temporal Point Processes and Their Applications](https://mlsanity.com/paper/1708.02647/) (2017)
- [From Language Models over Tokens to Language Models over Characters](https://mlsanity.com/paper/2412.03719/) (2024)
- [Interpreting Attention Models with Human Visual Attention in Machine Reading Comprehension](https://mlsanity.com/paper/2010.06396/) (2020)
- [Large Language Models Are Human-Like Internally](https://mlsanity.com/paper/2502.01615/) (2025)
- [Generalized Measures of Anticipation and Responsivity in Online Language Processing](https://mlsanity.com/paper/2409.10728/) (2024)
- [On the Role of Context in Reading Time Prediction](https://mlsanity.com/paper/2409.08160/) (2024)
- [User-Dependent Neural Sequence Models for Continuous-Time Event Data](https://mlsanity.com/paper/2011.03231/) (2020)
- [ScanDL: A Diffusion Model for Generating Synthetic Scanpaths on Texts](https://mlsanity.com/paper/2310.15587/) (2023)
- [Modeling Human Reading with Neural Attention](https://mlsanity.com/paper/1608.05604/) (2016)
- [A space-time conditional intensity model for invasive meningococcal disease occurrence](https://mlsanity.com/paper/1508.05740/) (2015)
- [The Effect of Surprisal on Reading Times in Information Seeking and Repeated Reading](https://mlsanity.com/paper/2410.08162/) (2024)

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

- [On the Proper Treatment of Units in Surprisal Theory](https://mlsanity.com/paper/2604.28147/) (2026)
- [Probing for Reading Times](https://mlsanity.com/paper/2604.18712/) (2026)
- [Revisiting the Estimation of Unigram Surprisal in Computational Psycholinguistics](https://mlsanity.com/paper/2610.18414/) (2026)

## Abstract (arXiv)

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.