- 3/28 IBACS/BIRC Talk: Dr. Ping Li
IBACS/BIRC Talk: Dr. Ping LiThursday, March 28th, 202409:00 AM - 10:30 AM
We are excited to announce the next talk in the IBACS/BIRC speaker series. Our next speaker of the semester is Ping Li from Hong Kong Polytechnic University. Ping Li, PhD, is Sin Wai Kin Professor in Humanities and Technology, Chair Professor of Neurolinguistics and Bilingual Studies, and Dean of the Faculty of Humanities at the University. He previously served as President of the Society for Computation in Psychology and Program Director at the U.S. National Science Foundation while being a Professor of Psychology, Linguistics, and Information Sciences at the Pennsylvania State University. Li’s research is focused on investigating the neurocognitive and computational bases of language acquisition, bilingualism, and reading comprehension in both children and adults. He uses digital technologies and cognitive neuroscience methods to study neuroplasticity and individual differences in learning to understand the relationships among language, culture, technology, and the brain. Li is currently Editor-in-Chief of Brain and Language and Senior Editor of Cognitive Science. He is a Fellow of the American Association for the Advancement of Science (AAAS).
Format: Virtual on Zoom or join the in-person watch party in Arjona 339 with coffee and donuts!
*Note that you must register to obtain the Zoom meeting details. Please use your University email address
Talk Title: Naturalistic Reading Comprehension in L1 and L2: What can “model-brain alignment” tell us about its neurocognitive mechanisms
Abstract: With the rapid developments in generative AI and large language models (LLMs), researchers are assessing the impacts that these developments bring to various domains of scientific studies. In this talk, I describe the “model-brain alignment” approach that leverages the progress in LLMs. Along with recent proposals on shared computational principles in humans and machines for naturalistic comprehension (e.g., listening to stories, watching movies), we use model-brain alignment to study naturalistic reading comprehension in both native (L1) and non-native (L2) languages. By training LLM-based encoding models on brain responses to text reading, we can evaluate (a) what computational properties in the model are important to reflect human brain mechanisms in language comprehension, and (b) what model variations best reflect human individual differences during reading comprehension. Our findings show that first, to capture the differences in word-level processing vs. high-level discourse integration, current LLM-based models need to incorporate sentence prediction mechanisms on top of word prediction, and second, variations in model-brain alignment allow us to predict L1 and L2 readers’ sensitivity to text properties, cognitive demand characteristics, and ultimately their reading performance. Overall, our work highlights the utility of the model-brain alignment approach in the study of naturalistic reading comprehension at multiple levels of cognitive processing and multiple dimensions of individual variation.Contact Information: