g-Rel-READER: A Dataset for Relevance and Reading Evaluation through Advanced Data from Eye-tracking and EEG Recordings
Year: 2025
Type: conference-paper
Abstract: Information relevance judgment is a fundamental cognitive decision performed by users during information seeking and reading tasks.Inferring relevance decisions from neurophysiological signals, particularly through eye-tracking and EEG recordings, opens possibilities for real-time relevance detection.To support this line of research, we present g-Rel-READER, a unique dataset comprising eye-tracking and EEG recordings collected during reading and relevance judgment of short news stories in a question-answering task.The dataset provides screen coordinates for each word, enabling temporal and spatial alignment between eye-tracking data, EEG signals, and the specific words being read.Question-relevant words are explicitly marked, facilitating analysis of relevance judgment processes.
Authors: Jacek Gwizdka, Michael Cole
Institution: The University of Texas at Austin
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4409973541