Yukie Nagai WEBSITE, The University of Tokyo International Research Center for Neurointelligence
Cognitive Development Based on Predictive Coding
A theoretical framework called predictive coding suggests that the human brain works as a predictive machine. That is, the brain tries to minimize prediction errors by updating the internal model and/or by affecting the environment. We have been investigating to what extent the predictive coding theory accounts for human cognitive development in terms of the temporal continuity (i.e., from non-social to social development) and the individual diversity (i.e., differences between typical development and developmental disorders).
This talk presents computational neural networks we designed to examine whether and how the process of minimizing prediction errors lead to cognitive development. Our experiments using a robot demonstrated that various cognitive abilities such as goal-directed action, imitation, estimation of others’ intention, and altruistic behavior emerged in a staged manner as observed in infants. Not only the characteristics of typical development but also those of developmental disorders such as autism spectrum disorder were generated as a result of aberrant prediction abilities. These results demonstrate that predictive coding provides a unified computational account for cognitive development (Nagai, Phil Trans B 2019).
Yukie Nagai is a Project Professor at International Research Center for Neurointelligence, the University of Tokyo. She received her Ph.D. in Engineering from Osaka University in 2004 and worked as a Postdoc Researcher at National Institute of Information and Communications Technology (NICT) and at Bielefeld University for five years. She then became a Specially Appointed Associate Professor at Osaka University in 2009 and a Senior Researcher at NICT in 2017. Since April 2019, she is working with the University of Tokyo.
Yukie Nagai has been investigating underlying neural mechanisms for social cognitive development by means of computational approaches. She designs neural network models for robots to learn to acquire cognitive functions such as self-other cognition, estimation of others’ intention and emotion, altruism, and so on. Her developmental theory based on predictive coding accounts for both continuity and diversity of cognitive development. She serves as the research director of JST CREST Cognitive Mirroring since December 2016.