UC San Diego

Research in reading comprehension traditionally relied on the experimental setting of word-by-word presentation which eventually revealed many neural biomarkers as well as establishing the basis of modern reading research. Since the development of brain-computer techniques and computational methods in the past decades, it has become possible to study reading comprehension in natural settings. This study used a variety of advanced technologies to analyze a dataset collected by the ZuCo group regarding reading comprehension. With natural language processing tools, we extracted the words essential for understanding sentences, and identified eye-tracking patterns that relate to these words. Using the EEG time series and frequency series, we also looked at neural patterns associated with those words and tried to build up a statistical model and neural network model that predicted the linguistic patterns. Consequently, the study is likely to provide new insights into future cognitive linguistics and brain-computer interaction research, which may help advance reading-aid technologies.