🤖 AI Summary
How can cognitive states and their dynamic evolution be uniformly and computationally represented, encompassing concepts, events, perceptual inputs, and task context, while supporting belief updating and interaction with working memory? This work proposes a typed weighted graph–based framework for cognitive state representation, in which nodes may embed nested subgraphs and edges encode six semantic relation types. The framework distinguishes between a long-term belief graph and a capacity-limited working memory graph, and formally defines their interaction mechanisms. It establishes the first unified cognitive state ontology, subsuming major cognitive architectures—including ACT-R, Soar, and Global Workspace Theory—as special cases. Furthermore, it enables formal diagnosis and analysis of cognitive phenomena such as conflict, coherence, and self-processing through a toolkit of graph-theoretic operators.
📝 Abstract
We present NEST (Nested Episodic State Topology), a foundational graph-theoretic representational ontology for modeling cognition as structured state formation and transformation rather than as a finished empirical model. Concepts, episodes, percepts, and task contexts are represented as typed, weighted graphs whose nodes may carry internal subgraph payloads; edges are typed under six relation classes -- causal, containment, temporal, associative, evidential, and spatial. Durable belief graphs are separated from capacity-limited working-memory graphs that may host transient non-belief content. WM-belief grounding, conflict catalogs, and belief-update operators specify how transient structure is tested against stored knowledge and how belief is revised. A reusable operator toolkit -- activation, graph-property functionals, working-memory transitions, awareness and trajectory functionals, and belief update -- organizes the formal core. Derived diagnostics such as fragmentation, involvement, signed evaluation, coherence, and active conflict define familiar phenomena in the same ontology; self-related processing is modeled through designated self-image subgraphs within belief. Subsequent sections instantiate this core without new primitives: phenomena signatures, a task-instantiation schema for action selection and failure modes, and compatibility mappings that embed ACT-R, Soar, Sigma, the Common Model of Cognition, Global Workspace Theory, semantic networks, Theory-Theory, and chunking as constrained regions of one language. Mappings constitute the culminating technical section; discussion addresses scope, limitations, and open research directions. The contribution is intentionally foundational: a transparent representational substrate for later empirical, computational, and domain-specific work.