基于眼动的读者识别和文本理解评价模型
A Discriminative Model for Identifying Readers and Assessing Text Comprehension from Eye Movements
摘要：
我们研究了通过观察阅读者的眼动来推断阅读者的身份和估计他们的文本理解水平的问题。我们开发了一个生成模型的个人凝视模式（扫描路径），利用词汇功能的固定的话。使用这个生成模型，我们推导出一个费舍尔分数表示的眼动序列。我们研究了一个Fisher核的Fisher-SVM和几种参考方法是否能够识别读者，并根据眼动数据估计他们的文本理解水平。虽然没有一种方法能够准确地估计文本理解，但我们发现，具有Fisher核的SVM在识别读者方面表现出色。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
