Division of Psychology and Language Science, Department of Experimental Psychology, Institute of Behavioural Neuroscience, University College London, London, UK.
Clinical Psychology, Central Institute of Mental Health, University of Heidelberg, J5, 68159, Mannheim, Germany.
Sci Rep. 2022 Sep 5;12(1):15086. doi: 10.1038/s41598-022-17747-2.
Much of our long-term knowledge is organised in complex networks. Sleep is thought to be critical for abstracting knowledge and enhancing important item memory for long-term retention. Thus, sleep should aid the development of memory for networks and the abstraction of their structure for efficient storage. However, this remains unknown because past sleep studies have focused on discrete items. Here we explored the impact of sleep (night-sleep/day-wake within-subject paradigm with 25 male participants) on memory for graph-networks where some items were important due to dense local connections (degree centrality) or, independently, important due to greater global connections (closeness/betweenness centrality). A network of 27 planets (nodes) sparsely interconnected by 36 teleporters (edges) was learned via discrete associations without explicit indication of any network structure. Despite equivalent exposure to all connections in the network, we found that memory for the links between items with high local connectivity or high global connectivity were better retained after sleep. These results highlight that sleep has the capacity for strengthening both global and local structure from the world and abstracting over multiple experiences to efficiently form internal networks of knowledge.
我们的许多长期知识都是以复杂网络的形式组织起来的。人们认为睡眠对于抽象知识和增强重要项目的记忆以实现长期保留至关重要。因此，睡眠应该有助于网络记忆的发展和其结构的抽象，以实现高效存储。然而，这一点尚不清楚，因为过去的睡眠研究主要集中在离散的项目上。在这里，我们探索了睡眠（25 名男性参与者的夜间睡眠/白天唤醒的within-subject 范式）对图形网络记忆的影响，在这些网络中，由于密集的局部连接（度数中心性）或由于更大的全局连接（接近度/中间中心性），某些项目很重要。通过不明确表示任何网络结构的离散关联来学习一个由 27 个行星（节点）稀疏连接的 36 个传送器（边缘）的网络。尽管对网络中的所有连接都有相同的接触，但我们发现，在睡眠后，具有高局部连通性或高全局连通性的项目之间的连接的记忆保留更好。这些结果表明，睡眠有能力从世界中增强全局和局部结构，并从多个经验中抽象出来，以有效地形成内部知识网络。
