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原文連結
論文資訊
- 類型：已發表論文
- 日期：2025-01-24
摘要
From sequences of discrete events, humans build mental models of their world. Referred to as graph learning, the process produces a model encoding the graph of event-to-event transition probabilities. Recent evidence suggests that some 網絡s are easier to learn than others, but the 神經 underpinnings of this effect remain unknown. Here we use fMRI to show that even over short timescales the 網絡 structure of a temporal sequence of stimuli determines the fidelity of event representations as well as the dimensionality of the space in which those representations are encoded: when the graph was modular as opposed to lattice-like, BOLD representations in visual areas better predicted trial identity and displayed higher intrinsic dimensionality. Broadly, our study shows that 網絡 context influences the
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