论文标题
使用半解释的卷积神经网络从眼神的相关性预测
Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks
论文作者
论文摘要
我们提出了一种图像分类方法，以预测眼神移动中文本文档的感知性。进行了一项引人注目的研究，参与者阅读了简短的新闻文章，并将其评为相关或与回答触发问题无关。我们将参与者的眼动扫描编码为图像，然后使用这些扫描路径图像训练卷积神经网络分类器。训练有素的分类器用于预测参与者对相应扫描仪图像的新闻文章的联系。此方法是独立的，因为分类器不需要了解屏幕包含或用户的信息任务。即使数据很少，图像分类器也可以预测具有多达80％精度的感知权利。与文献相似的眼睛跟踪研究相比，这种扫描图像分类方法优于先前通过可观的边距报道的指标。我们还试图解释图像分类器如何在相关和无关的文档上区分扫描路径。
We propose an image-classification method to predict the perceived-relevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents.
