arXiv 2512.026712025-12q-bio.NC

将度量转换为机制：高阶信息的认知相关性

Inferring Mechanisms from Bits: Promises and Pitfalls of Higher-Order Information in Cognitive Science

Brain and Cognition Center, University of Amsterdam（阿姆斯特丹大学脑与认知中心）；Department of Psychology, University of Cambridge（剑桥大学心理学系）；Department of Psychology, Queen Mary University of London（伦敦大学女王学院心理学系）；Department of Computing, Imperial College London（帝国理工学院计算系）；Vermont Complex Systems Institute, University of Vermont（佛蒙特大学佛蒙特复杂系统研究所）；Department of Psychological and Brain Sciences, Indiana University（印第安纳大学心理与脑科学系）；School of Psychology and Neuroscience, University of Glasgow（格拉斯哥大学心理与神经科学学院）；CINPSI Neurocog, Faculty of Health Sciences, Universidad Católica del Maule（马乌莱天主教大学健康科学学院CINPSI神经认知）；Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki（赫尔辛基大学生命科学研究所神经科学中心）

D. Rebbin, K. J. A. Down, T. F. Varley, R. Ince, A. Canales-Johnson

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AI总结：

本文探讨了高阶信息理论在神经科学中的应用，通过系统化多变量度量并分析冗余-协同平衡，提出优化多尺度复杂性的计算-通信权衡，并展望了未来在跨尺度桥梁、干预验证和热力学统一方面的研究方向。

摘要：

高阶信息理论已成为计算神经科学中迅速增长的工具包，受多变量依赖性可以揭示神经计算和通信方面信息的观念推动。然而，功能上对协同和冗余的解释往往快于有原则地论证统计量如何映射到机制性认知过程。在这里，我们回顾了主要的高阶度量家族，明确目标是将数学属性转化为有根据的机制性推断。首先，我们系统化了基于香农的多变量度量，并证明高阶依赖性可以用两个大致独立的轴来简洁地描述：相互作用强度和冗余-协同平衡。我们主张通过平衡协同整合和冗余广播的层次结构来优化多尺度复杂性，正式化一种计算-通信权衡。然后我们考察了部分信息分解，并概述了其在神经数据中的部署的务实考虑。借助相关的数学基础，我们将冗余-协同平衡与认知功能联系起来，通过逐步将它们的数学属性嵌入现实约束中，从小型合成系统开始，逐步构建到神经影像学。最后，我们指出机制性洞察的关键未来方向：跨尺度桥梁、基于干预的验证，以及热力学基础的统一信息动态。

英文摘要：

Higher-order information theory has become a rapidly growing toolkit in computational neuroscience, motivated by the idea that multivariate dependencies can reveal aspects of neural computation and communication that are invisible to pairwise analyses. Yet functional interpretations of synergy and redundancy often outpace principled arguments for how statistical quantities map onto mechanistic cognitive processes. Here we review the main families of higher-order measures with the explicit goal of translating mathematical properties into defensible mechanistic inferences. First, we systematize Shannon-based multivariate metrics and demonstrate that higher-order dependence is parsimoniously characterized by two largely independent axes: interaction strength and redundancy-synergy balance. We argue that balanced layering of synergistic integration and redundant broadcasting optimizes multiscale complexity, formalizing a computation-communication tradeoff. We then examine the partial information decomposition and outline pragmatic considerations for its deployment in neural data. Equipped with the relevant mathematical essentials, we connect redundancy-synergy balance to cognitive function by progressively embedding their mathematical properties in real-world constraints, starting with small synthetic systems before gradually building up to neuroimaging. We close by identifying key future directions for mechanistic insight: cross-scale bridging, intervention-based validation, and thermodynamically grounded unification of information dynamics.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。