There is a growing interest in the combined use of NLP and machine learning
methods to predict gaze patterns during naturalistic reading. While promising
results have been obtained through the use of transformer-based language
models, little work has been undertaken to relate the performance of such
models to general text characteristics. In this paper we report on experiments
with two eye-tracking corpora of naturalistic reading and two language models
(BERT and GPT-2). In all experiments, we test effects of a broad spectrum of
features for predicting human reading behavior that fall into five categories
(syntactic complexity, lexical richness, register-based multiword combinations,
readability and psycholinguistic word properties). Our experiments show that
both the features included and the architecture of the transformer-based
language models play a role in predicting multiple eye-tracking measures during
naturalistic reading. We also report the results of experiments aimed at
determining the relative importance of features from different groups using
SP-LIME.

本文报告了两个眼动追踪语料库和两种语言模型（BERT 和 GPT-2）的实验结果。实验表明，预测自然阅读过程中多种眼动追踪参数时，包含的特征和基于 transformer 的语言模型的架构都发挥了作用。同时通过 SP-LIME，实验分析了不同特征组的相对重要性。

衡量（心理）语言和易读性特征以及它们的溢出效应对预测眼动模式的影响

Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns

We study the problem of inferring readers' identities and estimating their
level of text comprehension from observations of their eye movements during
reading. We develop a generative model of individual gaze patterns (scanpaths)
that makes use of lexical features of the fixated words. Using this generative
model, we derive a Fisher-score representation of eye-movement sequences. We
study whether a Fisher-SVM with this Fisher kernel and several reference
methods are able to identify readers and estimate their level of text
comprehension based on eye-tracking data. While none of the methods are able to
estimate text comprehension accurately, we find that the SVM with Fisher kernel
excels at identifying readers.

研究读者通过阅读时眼动观察来推断其身份并估计文本理解水平的问题，通过使用词法特征的个体注视模式（扫描路径）的生成模型，得出基于眼动数据的 Fisher-SVM 的 Fisher 核方法，结果表明 Fisher-SVM 在识别读者身份上表现出色。