| Title | Dynamic emergence of relational structure network in human brains |
| Authors | Ren, Xiangjuan Zhang, Hang Luo, Huan |
| Affiliation | Peking Univ, Sch Psychol & Cognit Sci, Beijing, Peoples R China Peking Univ, Beijing Key Lab Behav & Mental Hlth, Beijing, Peoples R China Peking Univ, Peking Tsinghua Ctr Life Sci, Beijing, Peoples R China Peking Univ, PKU IDG McGovern Inst Brain Res, Beijing, Peoples R China Peking Univ, Key Lab Machine Percept, Minist Educ, Beijing, Peoples R China Chinese Inst Brain Res, Beijing, Peoples R China |
| Keywords | BAYESIAN MODEL SELECTION NEURAL MECHANISMS COGNITIVE MAP REPRESENTATIONS ATTENTION KNOWLEDGE TESTS |
| Issue Date | Dec-2022 |
| Publisher | PROGRESS IN NEUROBIOLOGY |
| Abstract | Reasoning the hidden relational structure from sequences of events is a crucial ability humans possess, which helps them to predict the future and make inferences. Besides simple statistical properties, humans also excel in learning more complex relational networks. Several brain regions are engaged in the process, yet the timeresolved neural implementation of relational structure learning and its contribution to behavior remains unknown. Here human subjects performed a probabilistic sequential prediction task on image sequences generated from a transition graph-like network, with their brain activities recorded using electroencephalography (EEG). We demonstrate the emergence of two key aspects of relational knowledge - lower-order transition probability and higher-order community structure, which arise around 540-930 ms after image onset and well predict behavioral performance. Furthermore, computational modeling suggests that the formed higher-order community structure, i.e., compressed clusters in the network, could be well characterized by a successor representation operation. Overall, human brains are computing the temporal statistical relationship among discrete inputs, based on which new abstract graph-like knowledge could be constructed. |
| URI | http://hdl.handle.net/20.500.11897/668191 |
| ISSN | 0301-0082 |
| DOI | 10.1016/j.pneurobio.2022.102373 |
| Indexed | SCI(E) |
| Appears in Collections: | 心理与认知科学学院 行为与心理健康北京市重点实验室 生命科学学院 机器感知与智能教育部重点实验室 |
