Welcome to the Predictive Cognition Lab

Our research focuses on identifying the neural mechanisms that integrate sensory signals and prior expectations in cognition. We aim to understand how humans adapt to changing contexts, learn from experiences, and generalize prior knowledge to new sensory inputs under uncertainty. To explore these questions, we combine computational modeling based on deep neural nets and large language models with behavioral and neuroimaging techniques, including eye tracking, pupillometry, EEG, and structural and functional MRI (s/fMRI). Predictive processing, particularly predictive coding, offers a potential framework for understanding adaptive behavior. By testing these theories against alternative models in areas like learning, perception, and memory, my research seeks to uncover the neuro-computational mechanisms underlying human cognition, specifically in speech and face perception. Additionally, our work explores individual differences in the weighting of prior expectations versus new sensory information, with the goal of applying our findings to clinical populations. Overall, our research supports the notion that the brain actively learns and applies prior knowledge from experience and context to facilitate successful perception under uncertainty in social interactions.