Pattern Constellations Research

1. Main Point, Scope and Method: Western philosophy treats pattern and recognition as two separate things. There's the objective pattern—the structure out there in the world. Then there's recognition—the mental event where someone or something registers that pattern. Two things that need connecting; the impossible to escape cartesian split into "world" and "mind". Where's the boundary? The pattern without anyone recognizing it—what is that? Recognition that isn't recognition of anything—is that recognition? You can't pull them apart.

This research proposes they're the same event, learned as what we call "Pattern Constellations". This isn't a theory to prove, although it seems to be a strong theoretical foundation. It's a lens. Does looking through it make problems disappear?

We look at how patterns emerge through repetition in both biological and artificial neural systems. We analyze semantic patterns in language models: literal word uses cluster tight, figurative uses spread out. This rod/cap manifold structure shows something about how meaning works. We test whether this perspective dissolves classical philosophical problems: consciousness, causation, symbol grounding, reference.

This is philosophical scafolding and not a tehory. Although a formalization is attempted, this is done for reasons of clarity (learning to speak the language of patterns).

Research Areas and Navigation: Three domains: philosophy (metaphysics, epistemology, philosophy of mind), cognitive science (neural pattern-recognition, embodied cognition, consciousness studies), and computer science (LLM architectures, semantic embeddings, information theory).

They converge on one empirical finding: meaning manifests as geometric topology in high-dimensional spaces.

Four areas organize the work. Core theory develops the pattern-constellation framework and PRU principle. Problem-dissolutions show how traditional puzzles vanish when you stop splitting pattern from recognition. Empirical evidence examines rod/cap structures in language models and convergent learning mechanisms. Connections explore resonances with Eastern non-dualism, implications for AI consciousness, applications to aesthetics and ethics.

The point isn't adding complexity. It's recognizing simplicity. Patterns all the way down. Recognition is the pattern itself, not something done to it.

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