Neural dynamics of reconstructing knowledge structure from temporally disordered inputs

Humans possess a remarkable ability to integrate fragmented experiences into coherent internal models. However, the neural dynamics supporting structure reconstruction from temporally disordered inputs remain poorly understood. Here, we show that knowledge reconstruction follows a delayed, two-stage neural trajectory.

Two independent cohorts learned complex physics concepts from scrambled video lectures during behavioral testing or functional magnetic resonance imaging. Learners receiving disordered inputs required sufficient information accumulation before their internal models matched the expert-defined structure. The posterior cingulate cortex showed stronger correspondence with knowledge structure during updating, whereas the precuneus showed more sustained correspondence after reorganization.

Functional coupling between these two regions predicted immediate learning success for disordered input, but this success did not fully translate into robust delayed retention. These findings reveal a two-stage neural mechanism for transforming fragmented inputs into coherent understanding, highlighting both the adaptive capacity and potential subsequent cost of learning from fragmented materials. This work was supported by the National Natural Science Foundation of China (62293550, 62293551).

State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China Xinran Xu, Xiangyu He, Xiaodan Feng & Chunming Lu Institute of Brain and Psychological Sciences, Sichuan Normal University, Chengdu, China The authors declare no competing interests. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

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To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Xu, X., He, X., Zhou, S. et al. Neural dynamics of reconstructing knowledge structure from temporally disordered inputs.