Language, Consciousness, and Manifestation: Towards a Pragmatic-Cognitive Framework for Next-Generation Neural Language Systems
DOI:
https://doi.org/10.65455/cnbj7s21关键词:
Language, Consciousness, Manifestation, Pragmatics, Cognition, Natural Language Processing, Neural Language Systems摘要
This paper proposes a geometric model of language and consciousness designed to speak simultaneously to linguistic theory and neural language systems. Conscious experience is modelled as a multidimensional sphere of concurrently available potentials—perceptions, emotions, intentions, and pre-verbal structures—while language is treated as a tangent drawn along the sphere’s surface. On this view, an utterance never exhausts a speaker’s inner state; it selects a thin, ordered path across a much richer configuration, necessarily highlighting some regions while leaving others implicit. Building on this model, the paper reframes speech acts, dialogue, public speaking, divination, and artistic creation as different ways of stabilising and steering what appears on the surface of the sphere. Manifestation is defined as the family of processes by which internal potentials become publicly available forms, of which linear language is only one special, highly constrained channel. Neural language systems are then reconsidered in this light: current architectures are powerful generators of tangents but possess no explicit representation of the spherical states, intentions, or collective fields from which human utterances emerge. The paper does not propose a specific architecture but outlines a pragmatic-cognitive framework in which future systems would model (i) intention as the choice of tangent direction, (ii) context as the evolving shape of individual and shared spheres, and (iii) attention as a mechanism for controlling which regions are allowed to manifest. By articulating language, consciousness, and manifestation within a single geometric metaphor, the paper aims to provide conceptual tools for rethinking both human communication and the design of next-generation neural language systems.