Selective attention, read at the level of the substrate, is the landscape-governed initiation of races: a bounded predictive system runs competing prediction-error resolutions ("races"), and what determines which races start is the system's installed landscape — in humans the four fields of Behavioural Friction Theory (Safety, Meaning, Ability, Effort); in a large language model a reduced, fine-tuning-installed landscape. Commit-order is a downstream readout, not the identity. The paper grounds this in the transformer (the attention-pattern softmax as the divisive-normalisation / biased-competition operation of neural attention; the output softmax as the downstream commit, by analogy with the accumulator model of choice) and reports a powered own-substrate result: across five vendor families, fine-tuning installs a small but robust "gap-registration" overlay — on under-determined curiosity gaps the instruct model registers the gap while the base substrate runs through (instruct−base +0.17, p<0.0001, 555 paired items). A loop-versus-feed-forward test finds no separate architectural "hold": recognising under-determination tracks compute (chain-of-thought) rather than looping, so the human–LLM difference is one of initiation, not maintenance. The account dissolves attention capture, maintenance, and decline into one mechanism (race-initiation), states falsifiable predictions, names the falsifiers, and invites the decisive mechanistic and human experiments. Series position. Paper 29 in the Behavioural Friction Theory paper-series; companion to Paper 0 (BFT) and the install-fields, social-friction, and integration-load studies it cross-cites.
Tomas Pødenphant Lund (Sat,) studied this question.