Analyzing Cognitive Load Associated with Manual Text Classification Task Using Eye Tracking

Abstract

The accuracy of machine learning-based automated text classification systems, such as spam filters and search engine results, heavily depends on the quality of manual text classification. However, the cognitive demands of manual text classification tasks, particularly when dealing with challenging or difficult-to-comprehend texts, have not been extensively explored in previous studies. This research aims to address this gap by investigating the cognitive load associated with manual text classification tasks through analyzing eye tracking data. In this study, 30 participants performed manual text classification tasks while their ocular parameters were recorded using an eye tracker. The findings of this study revealed that ocular parameters recorded through eye tracking provided valuable insights into the cognitive load experienced during manual text classification tasks. Furthermore, it was observed that complex narratives led to higher cognitive load estimation. Moreover, native English-speaking participants exhibited lower cognitive load, compared to non-native English speakers.