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#generative ai Open access

Why This Language? Historical Symmetry Breaking through Cultural Transmission

Sep 2026 · Zenodo (CERN European Organization for Nuclear Research)
Language and cultural evolution

Abstract

In iterated learning of emergent languages, cultural transmission can align semantic category systems and steer learners toward structured, efficient regions of language space. We show that limited transmitted evidence can also break symmetry among comparably structured solutions, biasing independent learners toward a shared, historically contingent reconstruction despite substantial item-level turnover. The target and strength of coordination are empirically distinguishable: developmental provenance can shift what learners reconstruct without necessarily changing how strongly they coordinate. In a controlled referential-game model, anchors captured from an immature snapshot of a parent's language steer descendants toward the immature organization; in the main GRU learner, this occurs without detectable weakening of coordination but at a cost to systematicity. We call this the Snapshot Effect. A second architecture and a minimal reconstruction model recover the same symmetry-breaking and provenance-target pattern. The local propagation mechanism is regime-dependent: in the compressed languages of the GRU learner, class-matched anchors preferentially stabilize untaught neighbouring meanings that use the same convention, whereas this mechanism does not carry over to a near-injective learner. Here, symmetry breaking refers to historical evidence resolving underdetermination among multiple viable structured reconstructions, not to competition among labels. Culture need not create structure. It makes one structured possibility historical. Working paper, version 1.0. 13 pages plus a 17-page supplement (full methods, every pre-registered outcome including failures, a minimal reconstruction model, figure legends). Code, registrations and a one-command replication driver: https://doi.org/10.5281/zenodo.22305564. Generative AI tools assisted with code, execution, review and editing; the author takes full responsibility.

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