The neural mechanisms of predictive coding during language comprehension

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During skilled reading, people recognize words from written character strings and combine words into a larger message-level interpretation. Reading is a pervasive part of modern human life, and impaired reading is highly detrimental to scholastic and professional success. Despite its importance, the neural processes that enable skilled readers to comprehend multi-word combinations remain poorly understood. In this research, Dr. Albert Kim and Dr. Phillip Gilley of the University of Colorado Boulder will use cutting edge neurophysiological methods to understand how the brain generates and uses predictions about the upcoming words within a text during reading, guided by prior linguistic context. Predictions are thought to support the fast pace of normal reading, which is typically 3-5 words per second, by allowing the brain to prepare in advance for anticipated text before it arrives in the retinal input. Predictions are also thought to be crucial to the common ability to perceive words accurately in the face of impoverished inputs, such as conditions of poor lighting, print quality, or visual acuity. The research in this project is expected to produce knowledge that guides the identification of reading disorders and distinctions between different sorts of disorders, which can in turn guide clinical and pedagogical approaches to reading disorders.The project will use scalp-recorded encephalography (EEG) to observe neural oscillatory activity -- reflecting the dynamic coupling and uncoupling of neural networks -- while healthy young adults read sentences. The sentences that participants read will be manipulated so that some sentence contexts render a specific word likely to occur, according to computational language models. Under these conditions, brain activity that occurs before and after the appearance of a word in the linguistic input will be examined for evidence of predictions. The project will investigate the role of neural oscillations in conveying predictions about upcoming words from high level brain regions to low level visual cortical areas and also in assessing the match between predictions and the bottom-up sensory input. The researchers will also study the types of linguistic contexts and processing demands that engage such predictive activity. Results of this research are expected to contribute significantly to cognitive neuroscience by combining three critical areas of research that have typically been studied separately: language processing, prediction in cognition, and neural oscillations. The project will develop new analytic methods for characterizing neural oscillations in EEG data, which will be developed into a toolbox to be shared with other researchers. The project will also produce a rich database of brain activity during the reading of naturalistic stories, which will be shared with the scientific community as a resource for further research.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.