Behavioural Friction Theory decomposes the forces that govern a bounded decision system into a small set of cognitive fields — Safety, Meaning, Ability, and Effort. Are these fields intrinsic properties of a cognitive substrate, or the generic product of that substrate’s exposure to experience? This paper uses a large language model as a controllable test substrate to answer the question constructively: install a field, remove it, dose it, and read off the friction and race signatures the theory predicts. A 2+2 asymmetry that maps onto nature and nurture. The two value fields (Safety, Meaning) can be installed into a base model by fine-tuning on experience-like data, and they install as graded intrinsic directions rather than the ceiling-level role-play that prompting elicits. The two capacity fields (Ability, Effort) are not raised by this same value-disposition route: capacity is the substrate’s intrinsic capacity-against-demand match (the cross-substrate inverted-U), and a competence disposition fine-tune — carrying no skill-targeted training — yields only an expressive claim, not graded capability. (Skill-targeted training is a separate regime that can raise the ceiling; the claim here is the narrow one.) The bridge: nurture gates the realisation of a nature-fixed ceiling. An installed self-efficacy disposition gates how much of a fixed capacity is realised through a give-up / avoidance threshold while leaving latent capability untouched: fine-tuning toward helplessness lowers realised performance monotonically with model capacity (Qwen2.5 7B/14B/32B) while latent capability stays flat, and this gating is induced by fine-tuning where prompting cannot. The one boundary. A principal difference between this substrate and a human one, for growing these fields, is forward consolidation — the model cannot store experience forward across sessions — offered as a hypothesis with a named falsifier, alongside other standing differences (embodiment, online/recurrent control, neuromodulation, evolved fast-timing priors). The install machinery is the demonstration apparatus; the deflationary, substrate-universal field ontology is the contribution. Results are reported with informativeness-gated readouts, multi-model and cross-family replications, a second adapter seed, non-arithmetic generality checks, and a forced-answer (refusal-impossible) control that separates avoidance from competence loss. Companion papers in the series develop the field ontology and computational forms, the capacity inverted-U, the race-opening predicate, the competing-routes measurement-model programme, and the subtraction-control and dread/agent-loop accounts. Prepared for submission to a cognitive-science venue that uses large language models as a model of cognition. Data and code. The build scripts, notebooks, fine-tuning data generators, and per-run result files are released with the paper in the accompanying repository.
Tomas Pødenphant Lund (Sun,) studied this question.