Collaborative research: predictive processing in naturalistic language comprehension through EEG and computational modeling

Award Details: The National Science Foundation's Division of Behavioral and Cognitive Sciences awarded a $225,482 Project Grant to the University of Florida (Award Date: September 1, 2025; Completion Date: August 31, 2028) under the Social, Behavioral, and Economic Sciences program (CFDA 47.075). This collaborative research initiative investigates predictive processing in naturalistic language comprehension through electroencephalography (EEG) signals and computational modeling.

Products and Services: The project delivers a comprehensive research dataset comprising EEG brain signals collected from three groups of bilingual participants with varying proficiency levels as they listen to audiobook stories in their first and second languages. Primary deliverables include statistical analyses demonstrating alignment between linguistic features extracted through artificial intelligence language models and recorded brain activity, identifying which computational features best capture neural responses across different language backgrounds. The project produces a large corpus of brain and behavioral data made available to the broader scientific community to enhance transparency and reproducibility in language research. Additional outputs include educational programs targeting future teachers and the public to increase awareness of bilingual language processing. A sub-award to Universidad De Granada ($225,482 obligation with April 2, 2026 award date) contributes EEG data collection from approximately 80 Spanish learners of English, with participants completing language background questionnaires and proficiency assessments, overseen by Drs. Teresa Bajo and Daniela Paolieri in coordination with principal investigators at the University of Florida and University of Michigan.

Abstract

Humans understand language rapidly and with remarkable ease. One way that humans do this is by predicting what a conversation partner might say next. But, there remain many unanswered questions about how different language backgrounds might help, or hinder, effective predictions. There is a critical need to understand proficient second-language comprehension. This project studies brain signals while bilinguals listen to an audiobook story in their first and second language. Computational modeling using artificial intelligence (AI) language systems are used to test the kinds of predictions people make, the information that guides those predictions, and how predictions are affected by differences in language background. This project offers insight into how AI can incorporate multiple languages in a realistic way and increases awareness of bilingual language with programs targeting future teachers and the public. Other benefits to society include increased transparency and reproducibility in language research by providing a large corpus of brain and behavioral data for other scientists and engineers. To meet these aims, the project collects electroencephalography (EEG) signals from three groups of bilingual participants with different levels of experience while they listen to an audiobook story. These signals reflect fast-changing brain responses and are highly sensitive to expectations in language. AI is used to capture the linguistic features of the story, such as the relationships between nouns and verbs in a sentence and the predictability of upcoming words. Statistical analyses are used to test the alignment between these features and brain activity, showing which features best capture brain activity and how this may be different for different language backgrounds. By training computational models with different amounts of exposure to one or more languages, the project further tests how the statistical properties of different languages modulate brain responses of multilingual language users. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the foundation's intellectual merit and broader impacts review criteria.- subawards are planned for this award.