Tsinghua University Chair Professor of Sciences, Chief Scientist of Beijing Academy of ...
In recent years, fascinating progresses have been made in utilizing artificial intelligence to solve a broad range of problems, and many advances have been largely inspired by research of brain and cognitive sciences on biological brain. In our lab, we advocate further interactions between the fields of AI and cognitive neuroscience to benefit both fields.On one hand, there is great potential for cognitive neuroscience to benefit AI (cognitive-neuroscience-inspired AI). The structures and functions of neural systems, which result from hundreds of millions of years evolution, are optimized for animals processing information in order to survive in natural environments. They naturally serve as the resources inspiring us to develop next-generation AI.On the other hand, AI can make fundamental contributions to cognitive neuroscience as well (AI-inspired cognitive neuroscience). In addition to serving as advanced mathematical tools for analyzing big data in neuroscience, models of AI can also give us insight into understanding the inner mechanisms of biological brain and intelligence. More importantly, biological brains are the result of evolution; and analogically we can manipulate architectures and environments of AI to “re-run” evolution and therefore to pry open secrets that lead to the emergence of the human brain and mind.
event cognition - how people segment the continuous life stream into discrete subjectiv...
Psychometrics, diagnostic classification modeling, computerized adaptive testing, langu...
Leveraging machine learning methodology to make scientific discoveries in cognitive sci...
Reinforcement learning and cognitive evolution
Self organized critical state in brain and neural model of consciousness
Representation-based assessment on biological and artificial intelligence
AI+Psychometrics; Automatic text readability measure.
Reinforcement Learning and Evolutionary Algorithms
reinforcement learning and computational model.
Neural representation in advanced cognition activities of AI and human brain
Liu, J., Wu, S., Zhou, K., Song, Y., eds. (2021). Cognitive NeuroIntelligence. Lausanne: Frontiers Media SA. doi: 10.3389/978-2-88971-340-0.
Hao X, Huang T, Song Y, et al. Development of navigation network revealed by resting-state and task-state functional connectivity[J]. NeuroImage, 2021, 243: 118515.
Xu S, Li Y, Liu J. The Neural Correlates of Computational Thinking: Collaboration of Distinct Cognitive Components Revealed by fMRI[J]. Cerebral Cortex, 2021.
Tian X, Hao X, Song Y, et al. Homogenization of face neural representation during development[J]. Developmental Cognitive Neuroscience, 2021: 101040.
Yu M, Li X, Song Y, et al. Visual association learning induces global network reorganization[J]. Neuropsychologia, 2021, 154: 107789.