通过眼动轨迹判断读者是否读过同一文本，准确率显著提升。
Déjà Vu? Decoding Repeated Reading from Eye Movements
- 用眼动数据构建特征与神经模型，识别重复阅读
- 引入认知模型生成模拟眼动数据，增强模型性能
- 揭示记忆效应在眼动中的体现，适合阅读行为研究者
无论小说、新闻、菜谱还是学术论文，人们常常多次阅读同一文本。本文探讨能否仅凭眼动轨迹自动判断读者是否曾接触过该文本。我们提出两种任务变体，并通过特征工程与神经网络模型取得良好效果。进一步提出一种通用策略，利用认知模型生成的模拟眼动数据增强训练。分析表明，模型所依赖的信息揭示了记忆在重复阅读中的作用机制，同时为理解记忆如何影响阅读行为提供了新工具。本工作深化了对眼动如何反映先前文本暴露记忆效应的理解，为未来预测重复阅读行为的应用奠定基础。
原文摘要 · Abstract (English)
Be it your favorite novel, a newswire article, a cooking recipe or an academic paper -- in many daily situations we read the same text more than once. In this work, we ask whether it is possible to automatically determine whether the reader has previously encountered a text based on their eye movement patterns. We introduce two variants of this task and address them with considerable success using both feature-based and neural models. We further introduce a general strategy for enhancing these models with machine generated simulations of eye movements from a cognitive model. Finally, we present an analysis of model performance which on the one hand yields insights on the information used by the models, and on the other hand leverages predictive modeling as an analytic tool for better characterization of the role of memory in repeated reading. Our work advances the understanding of the extent and manner in which eye movements in reading capture memory effects from prior text exposure, and paves the way for future applications that involve predictive modeling of repeated reading.
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