Schedule of Events | Symposia

Predictive Pre-Activation During Language Comprehension Is Preserved in Older Adult Readers

Poster Session E - Monday, March 31, 2025, 2:30 – 4:30 pm EDT, Back Bay Ballroom/Republic Ballroom

Ryan Hubbard¹ (rhubbard2@albany.edu), Kara Federmeier²; ¹University at Albany, SUNY, ²University of Illinois, Urbana-Champaign

Language comprehension involves multiple processes, including the engagement of neural mechanisms to predict upcoming words in order to more quickly and efficiently comprehend information. Eye-tracking and ERP-based research have shown that effects of word predictability are reduced for older adult readers, but little work has investigated age-related effects on neural signals of pre-activation, measured prior to target words. In young adult readers, we previously used representational similarity analysis (RSA) to compare patterns of EEG activity before and after target words, and found evidence that word representations were pre-activated. Here, we used this RSA technique to re-analyze two combined EEG datasets with older adult readers (total N = 41) to determine if aging impacts pre-activation. Results showed that a pattern of pre-activation similar to that found in younger adult readers was present for older adults, although the overall magnitude of pattern similarity was reduced. Importantly, we used linear mixed-effects regression analyses to demonstrate that the magnitude of neural pre-activation was related to the predictability of the target word, suggesting predictive pre-activation of upcoming word features, a pattern also found in young adults. Unlike younger adults, older adults appeared to engage additional frontal cortical resources when predicting words, and the time-course of neural similarity changes differed between age groups. Overall, our results suggest that some forms of anticipatory processing during language comprehension are preserved with age, and highlight the importance of examining neural processing prior to predictable or unpredictable stimuli to better understand the mechanisms of prediction.

Topic Area: LANGUAGE: Semantic