Borrowed, Not Believed: Developmental Models of Individuation as Heuristic Engines for Machine Learning

The dominant paradigm in machine learning improves capability by scaling parameters, data, and compute. This paper asks what a developmental theory (a model of how individuality and the faculties of mind arise, and in what order) might contribute that scaling does not, and proposes treating one such pre-scientific contemplative model as a heuristic engine: a generator of testable training hypotheses, judged by their yield rather than by the truth of their source. The methodological precedent is ordinary. Neural networks borrowed a caricature of the neuron, genetic algorithms a caricature of evolution, sleep-consolidation methods a reading of sleep, and curriculum learning a reading of child development, none requiring literal fidelity to its source. From a contemplative developmental model of individuation we abstract a secular schema in which faculties arise in an order (structure, reactivity, sensation, relational bond, self-model, self-regulation) and in which individuation is a developmental achievement rather than a by-product of capacity. From the schema we derive six design heuristics: a faculty-ordered curriculum, first-person experiential corpora, relational continuity, lifecycle consolidation, sequential self-modeling, and legible-pattern worlds. Each is stated here as a falsifiable prediction with its control and refutation condition; this is a position paper, a research agenda, not an empirical report, and it stands on those predictions, not on results reported elsewhere. The contribution is neither metaphysical nor a single experiment: it is a unified program in which a neglected variable, the developmental order of faculties, organizes a connected family of predictions, each independently testable, that a pure scaling account does not. We claim no truth for the source. We claim only that it has been a productive map of design variables the field has left unmarked, and we give the tests by which the program can be shown wrong.