Cognitive Field Theory of Learning, Inference, and Emergence

Quantitative Biology > Neurons and Cognition [Submitted on 15 Jan 2026 (v1), last revised 31 May 2026 (this version, v3)] Title:Cognitive Field Theory of Learning, Inference, and Emergence View PDF HTML (experimental)Abstract:Learning, inference, memory, and emergence in biological and artificial systems are often described using disparate theoretical frameworks ranging from neural field models to recurrent and attention-based architectures. Here we develop a cognitive field theory in which cognition emerges as a collective nonequilibrium phenomenon governed by the infrared organization of adaptive dynamical time scales. Starting from a stochastic cognitive-field equation with homeostatic stabilization and adaptive manifold geometry, we show that collective cognitive dynamics is organized by slowly relaxing infrared modes embedded within a high-dimensional cognitive manifold. Integrating out latent slow-memory sectors generates retarded self-energy feedback and nonlocal memory kernels governing long-time contextual persistence and collective cognitive coherence. We introduce the time-scale density of states (TDOS) as a fundamental descriptor characterizing the distribution of collective relaxation modes underlying inference, memory, and adaptive reasoning. Learning and adaptation continuously reorganize the infrared TDOS, selectively stabilizing weakly damped collective sectors that support contextual organization and recursive collective dynamics. Near criticality, the infrared TDOS generically develops a broad and nearly flat structure associated with the accumulation of slowly relaxing collective modes, producing scale-free temporal organization and enhanced collective coherence. Within this framework, memory formation, adaptive reasoning, and emergent intelligence arise as hierarchical stages of collective infrared dynamical organization. Submission history From: Byung Gyu Chae [view email][v1] Thu, 15 Jan 2026 09:34:50 UTC (382 KB) [v2] Wed, 21 Jan 2026 06:47:17 UTC (387 KB) [v3] Sun, 31 May 2026 09:21:49 UTC (652 KB) References & Citations Loading... Bibliographic and Citation Tools Bibliographic Explorer (What is the Explorer?) Connected Papers (What is Connected Papers?) Litmaps (What is Litmaps?) scite Smart Citations (What are Smart Citations?) Code, Data and Media Associated with this Article alphaXiv (What is alphaXiv?) CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub (What is DagsHub?) Gotit.pub (What is GotitPub?) Hugging Face (What is Huggingface?) ScienceCast (What is ScienceCast?) Demos Recommenders and Search Tools Influence Flower (What are Influence Flowers?) CORE Recommender (What is CORE?) arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.