A Scaling Prediction for Global-Workspace Emergence in Language Models: A Preregistered Discriminating Test Derived from the Prima Conscientia Framework
Zenodo (CERN European Organization for Nuclear Research) July 15, 2026 Jeremy Allen Clements
A preregistered prediction distinguishes two accounts of why language models develop internal workspace-like structure. Under the Prima Conscientia framework, which views complex systems as expressions of a fundamental consciousness field, workspace structure should appear even in very small models and strengthen smoothly with scale. Under the rival complexity-emergence view, workspace properties should appear only in discontinuous jumps at capability thresholds. The prediction is decomposed into structural presence (the framework's central commitment) and downstream function (allowed to lag). A test protocol using the Jacobian-lens methodology and Pythia model suite is specified, with falsification conditions and integrity conditions applied symmetrically to confirming and disconfirming outcomes.