Our research lab investigates how human infants acquire social cognitive abilities through interactions with the environment by means of constructive approach. We design computational models (e.g., neural networks, Bayesian models, etc.) for robots to learn to communicate with others in order to reveal the underlying neural mechanisms of cognitive abilities.
As a key mechanism for development, we have proposed a computational theory based on predictive coding. It has been suggested that the human brain minimizes prediction error between incoming sensory signals and top-down prediction by updating the internal model and/or affecting the environment. In order to verify our theory and to understand to what extent the theory accounts for cognitive development, we have been designing robots that learn to recognize the self, differentiate the self from others, imitate others, share intentions and emotional states with others, help others, and so on, which appear at different ages in early infancy.
Furthermore, we develop assistant systems for developmental disorders. People with autism spectrum disorder (ASD) are known to suffer from hyper- and/or hypo-sensitivity in perception, which is hypothesized to cause their difficulties in social communication. We investigate underlying sensory and neural mechanisms for atypical perception by conducting computational cognitive experiments and design wearable simulators that allow typically developing people to experience the perceptual world of ASD. This approach contributes to deeper understanding of the underlying neural mechanisms for social cognitive development.
Please contact me if you are interested in our research activities and/or in joining our research group.
Yukie Nagai, Ph.D.
Project Professor, International Research Center for Neurointelligence, The University of Tokyo