Séminaire ayant lieu dans le cadre du Doctorat en informatique cognitive, en collaboration avec le centre de recherche CRIA            

TITRE : Forecasting Spoken Language Development

Patrick WONG

Jeudi le 17 septembre 2026 à 10h30

Local PK-5115 (Il est possible d'y assister en virtuel en vous inscrivant ici)               

RÉSUMÉ

Can bottom-up neural signals — brain data collected from infancy — forecast how spoken language develops, better than top-down clinical categories like diagnosis or demographics? Using MRI and EEG, we build predictive models of language outcomes that outperform standard predictors across typical, hearing-impaired, and autism-spectrum populations. Beyond prediction, these models probe how early cortical and subcortical processing grounds native and non-native speech perception, and how restored sensorimotor input, via cochlear implantation, recruits brain regions to support language. The work asks whether grounding language forecasts in neural data, rather than symbolic or demographic proxies, better captures how language actually emerges.

BIOGRAPHIE

Patrick C. M. WONG is Founding Director of CUHK's Brain and Mind Institute and Professor of Linguistics, Paediatrics, and Psychology, with research centered on how brain-based measures can forecast and inform interventions for spoken language development. His work is interdisciplinary, spanning infant and adult brain imaging, perceptual psychophysics, grammar learning, gene sequencing, and predictive modeling of developmental trajectories.

RÉFÉRENCES 

Wong, P. C. M., Pan, S., Lai, C. M., Chan, P. H. Y., Feng, G., Lam, H. S., Leung, T. Y., Novitskiy, N., & Leung, T. F. (2026). Speech auditory brainstem response to predict language delay. Pediatrics, 157(4), e2025073409.

Wang, Y., Yuan, D., Dettman, S., Choo, D., Xu, E. S., Thomas, D., Ryan, M. E., Wong, P. C. M., & Young, N. M. (2026). Forecasting spoken language development in children with cochlear implants using preimplant magnetic resonance imaging. JAMA Otolaryngology–Head & Neck Surgery, 152(3), 232–241.

Szot, A., Mazoure, B., Attia, O., Timofeev, A., Agrawal, H., Hjelm, D., Gan, Z., Kira, Z., & Toshev, A. (2025). From multimodal LLMs to generalist embodied agents: Methods and lessons. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 10644–10655.

Smith, L. B., Jayaraman, S., Clerkin, E., & Yu, C. (2018). The developing infant creates a curriculum for statistical learning. Trends in Cognitive Sciences, 22(4), 325–336. 

Best, C. A., Yim, H., & Sloutsky, V. M. (2013). The cost of selective attention in category learning: Developmental differences between adults and infants. Journal of Experimental Child Psychology, 116(2), 105–119.