From Human Reading to NLM Understanding: Evaluating the Role of Eye-Tracking Data in Encoder-Based Models

Cognitive signals, particularly eye-tracking data, offer valuable insights into human language processing. Leveraging eye-gaze data from the Ghent Eye-Tracking Corpus, we conducted a series of experiments to examine how integrating knowledge of human reading behavior impacts Neural Language Models (NLMs) across multiple dimensions: task performance, attention mechanisms, and the geometry of their embedding space. We explored several fine-tuning methodologies to inject eyetracking features into the models. Our results reveal that incorporating these features does not degrade downstream task performance, enhances alignment between model attention and human attention patterns, and compresses the geometry of the embedding space 1 .