arXiv:2506.19999cs.LGcs.CL2025-06ACL被引 2

用点过程建模阅读时的眼动,更精准捕捉注视与跳视的时空规律。

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

  • 基于带标记的时空点过程,同时建模注视位置、时长与跳视行为。
  • 模型对眼动数据的拟合优于基线,尤其在跳视模式上表现更优。
  • 发现语境意外性对注视时长预测提升有限,质疑其解释力。

阅读是空间与时间上交替发生的注视与跳视过程。传统方法依赖聚合眼动数据并施加强假设,忽略了丰富的时空动态。本文提出一种基于带标记的时空点过程的通用概率模型,不仅捕捉注视时长,还建模注视空间位置与发生时间。跳视通过霍克斯过程建模,反映每次注视对后续注视在时空上的激发效应;注视时长则通过注视特异性预测因子的时间卷积建模,捕捉跨时间的累积影响。实验表明,该模型对人类跳视行为的拟合优于基线模型。在注视时长预测中,引入语境意外性作为预测因子仅带来微弱改进,暗示意外性理论难以解释精细眼动行为。

原文摘要 · Abstract (English)

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.

眼动建模点过程阅读行为时空分析

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