从眼动中解码阅读目标

Decoding Reading Goals from Eye Movements

October 28, 2024

作者: Omer Shubi, Cfir Avraham Hadar, Yevgeni Berzak

cs.AI

摘要

读者在阅读文本时可能有不同的目标。 这些目标是否可以从他们在文本上的眼动模式中解码出来？在这项工作中，我们首次研究了是否可能解码日常生活中常见的两种阅读目标：信息搜索和普通阅读。利用大规模眼动跟踪数据，我们将广泛应用最先进的眼动和文本模型，涵盖不同的架构和数据表示策略，并进一步引入新的模型集成。我们系统地评估这些模型在三个泛化级别上的表现：新的文本项、新的参与者以及两者的组合。我们发现眼动包含了对这一任务非常有价值的信号。我们进一步进行了错误分析，基于先前关于普通阅读和信息搜索之间差异的经验发现，并利用丰富的文本注释。这种分析揭示了文本项和参与者眼动的关键特性，这些特性导致了任务的困难。

English

Readers can have different goals with respect to the text they are reading. Can these goals be decoded from the pattern of their eye movements over the text? In this work, we examine for the first time whether it is possible to decode two types of reading goals that are common in daily life: information seeking and ordinary reading. Using large scale eye-tracking data, we apply to this task a wide range of state-of-the-art models for eye movements and text that cover different architectural and data representation strategies, and further introduce a new model ensemble. We systematically evaluate these models at three levels of generalization: new textual item, new participant, and the combination of both. We find that eye movements contain highly valuable signals for this task. We further perform an error analysis which builds on prior empirical findings on differences between ordinary reading and information seeking and leverages rich textual annotations. This analysis reveals key properties of textual items and participant eye movements that contribute to the difficulty of the task.