This conceptual note proposes a model emphasizing human–AI interaction and temporal selection effects, suggesting new frameworks for understanding meaning emergence.
This conceptual note proposes a schematic model connecting structure, temporal selection, and human–AI externalization within a single compression–recursive framework. The note argues that meaning emerges on top of already accumulated structural regularities, and that past and future are not independent temporal domains in practice, but are recursively reconfigured through present action. The model distinguishes fixed past events from effective pasts and treats future representation as a present-acting variable rather than a mere forecast. It also frames human–AI interaction as an externalized structural mediation layer that may participate in the stabilization, amplification, and redirection of recursive temporal processes. The equations in this note are conceptual schemata rather than empirically validated dynamical laws. This note was developed with the assistance of ChatGPT for structuring, drafting, and language refinement. The conceptual direction, model design, and final editorial decisions are the author’s own.
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Hinano Kimura (2026) studied this question.
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