Nature and Nurture in a Language Model: Installable Value Fields, Intrinsic Capacity, and the Forward-Consolidation Boundary

Abstract

Behavioural Friction Theory decomposes the forces that govern a bounded decision system into four cognitive fields — Safety, Meaning, Ability, Effort. Are these fields intrinsic to a substrate, or the generic product of its exposure to experience? This paper uses a large language model as a controllable test substrate to probe the question: install a field, remove it, dose it, and read off the friction and race signatures the theory predicts. The two value fields (Safety, Meaning) install into a base model by experience-fine-tuning as graded, generalising dispositions. On a supplied forced choice this is not something a prompt cannot match — a rule-elicitation prompt reaches at least as far as the install on capable models; what fine-tuning does that no tested prompt does is build, at comprehension and upstream of any answer, the landscape structure that lets a race start (a content-neutral initiation effect, in a single tested frame). The two capacity fields (Ability, Effort) are not raised by this value-disposition route — and, by construction, could not be: raising a capacity ceiling by asserting competence is a category error, so this is a design boundary rather than a symmetric second empirical arm (the load-bearing evidence that capacity is intrinsic is an external cross-substrate inverted-U corpus, not this control). A bridge connects the two: an installed value-disposition (self-efficacy) gates how much of a fixed capacity ceiling is realised — fine-tuning toward helplessness lowers realised performance monotonically with model size while latent capability stays flat — where fine-tuning succeeds and prompting fails. A principal difference from a human substrate, with respect to growing these fields, is forward consolidation: the model cannot store experience forward across sessions. The paper is careful about what it settles. The substrate-general reading is advanced as a deflationary hypothesis with a named falsifier (a forward-consolidating, yoked-control test), not a substrate-universal law, and is positioned within Resource-Rational Analysis (Lieder & Griffiths, 2020). A persona-selection account — that fine-tuning elicits a latent pretrained character rather than installing a disposition — is a live alternative no single behavioural result here decisively excludes, though one comprehension-time result constrains it; the decisive mechanistic test (sparse-autoencoder model-diffing) is named as the most important open work. The install machinery is the demonstration apparatus; the deflationary hypothesis it makes testable is the contribution. v2 (August 2026) — repositioning revision. Situated within Resource-Rational Analysis; the substrate-general reading stated as a hypothesis with a named falsifier rather than a law; the capacity claim reframed as a category boundary; the citation practice de-farmed; plus an editorial pass. Claims are held to what the current data support, with several strengthening experiments named as future work. Earlier version remains in the version history.