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

TL;DR

This paper introduces a probabilistic spatio-temporal point process model for reading behavior that captures fixations, saccades, and durations, providing a more detailed understanding of eye movements during reading.

Contribution

It presents a novel Hawkes process-based model that accounts for the spatial and temporal dynamics of eye movements, improving over traditional aggregated approaches.

Findings

Hawkes process model fits human saccades better than baselines

Incorporating surprisal marginally improves fixation duration prediction

Model captures spillover effects in fixation durations

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.