Pigozzi Federico, Goldstein Adam, Levin Michael
Allen Discovery Center at Tufts University, Medford, MA, USA.
Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK.
Commun Biol. 2025 Jul 9;8(1):1027. doi: 10.1038/s42003-025-08411-2.
How does learning affect the integration of an agent's internal components into an emergent whole? We analyzed gene regulatory networks, which learn to associate distinct stimuli, using causal emergence, which captures the degree to which an integrated system is more than the sum of its parts. Analyzing 29 biological (experimentally derived) networks before, during, after training, we discovered that biological networks increase their causal emergence due to training. Clustering analysis uncovered five distinct ways in which networks' emergence responds to training, not mapping to traditional ways to characterize network structure and function but correlating to different biological categories. Our analysis reveals how learning can reify the existence of an agent emerging over its parts and suggests that this property is favored by evolution. Our data have implications for the scaling of diverse intelligence, and for a biomedical roadmap to exploit these remarkable features in networks with relevance for health and disease.
学习如何影响智能体内部组件整合为一个涌现的整体？我们使用因果涌现分析了基因调控网络，该网络学习将不同刺激联系起来，因果涌现捕捉了一个整合系统超越其各部分之和的程度。在训练前、训练期间和训练后分析29个生物学（实验得出）网络，我们发现生物网络因训练而增加了它们的因果涌现。聚类分析揭示了网络涌现对训练做出反应的五种不同方式，这些方式并不映射到表征网络结构和功能的传统方式，但与不同的生物学类别相关。我们的分析揭示了学习如何使一个智能体相对于其各部分的涌现存在具体化，并表明这一特性受到进化的青睐。我们的数据对各种智能的扩展以及对利用与健康和疾病相关的网络中这些显著特征的生物医学路线图具有启示意义。
