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Trace & Trajectory Semantics: Meaning Dynamics in Pre-Representational Space

Publié
Serveur de preprints
Preprints.org
DOI
10.20944/preprints202510.2495.v1

This paper proposes Trace & Trajectory (T&T) Semantics, a pre-representational framework for understanding meaning as intent-driven navigation through informational space. Motivated by fieldwork with multimodal, intersubjective communication—where meaning emerges through gesture, prosody, and embodied coordination rather than propositional structures—I extend Hoffman and Prakash's trace logic to continuous semantic trajectories. The framework models meaning not through Euclidean feature spaces but through attractor dynamics: meaning stabilizes where intent-driven trajectories converge under dissipative constraints, creating basins that guide navigation without representational anchoring. The critical innovation is operator σ's fractal architecture. As meta-awareness intensifies, trace patterns achieve self-similarity across scales, enabling collapse and reconjunction without infinite regress. This mechanism naturalizes prototype effects, conceptual metaphor, image schema stability, and abstract reasoning as emergent from how conscious agents navigate meaning-space under intent, dissipation, and σ-modulation—not from mental representations. T&T dissolves the hard problem of semantic content by grounding meaning in informational dynamics during concrete intersubjective engagement, where patterns maintain semiotic coherence through intent-driven navigation, without reference to external representational targets. This preserves systematicity while respecting embodied intuition. The framework offers cognitive linguists, anthropologists, and semantic theorists an approach that is formally rigorous (utilizing attractor dynamics, Markov kernels, and σ-operators), empirically tractable (applicable to actual discourse and interaction), and phenomenologically adequate. Crucially, the formalism describes patterns in conscious, intentional dynamics—not neural mechanisms—making it appropriate for phenomena in which agent purpose drives semantic organization.

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