The Physics of Learning: How Race-Architecture Constraints Explain What We Know About Teaching, Communication, and Understanding

Paper 16 · Pødenphant Lund, T. (2026) · Preprint · Live on Zenodo

A didactic translation of Friction Theory mechanics to human learning, teaching, and communication. Three traditions of learning science are each empirically excellent and mechanically thin: cognitive load theory and multimedia learning (Sweller, Mayer); desirable difficulties and retrieval practice (Bjork, Roediger, Karpicke); psychological safety and need-prepotency (Maslow, Edmondson). This paper proposes they are local consequences of the same substrate-universal physical constraints: bounded-capacity race-architecture, friction as the cost of unresolved competing routes, hysteresis as encoding-through-loading, and the Net Friction Rule. For audiences in education research, instructional design, and organisational psychology.

DOI (concept)	10.5281/zenodo.20416959
Status	v2 live on Zenodo (2026-05-29); v3 in preparation
Audience	Education research, instructional design, organisational psychology
Author	Tomas Pødenphant Lund [ORCID]

Three central commitments

Substrate-mechanical account of established didactics

Race-architecture (R1–R3), friction as competing-route cost, hysteresis as encoding-through-loading, and the Net Friction Rule are developed for a non-AI / non-FT-familiar audience. The substrate-level account derives, rather than postulates, five classical findings of learning science:

• Working-memory limits — capacity is a structural consequence of bounded compute over race-architecture, not a brute empirical fact.
• The testing effect — encoding happens through loading, not from passive exposure.
• Spacing and interleaving effects — commit-against-decay vs commit-against-confusion friction-profiles.
• Expertise reversal — prior commitments raise the matched-friction window upward.
• Prepotency — safety-field activity competes for race-allocation regardless of substantive-content needs.

LLM substrates as mechanical mirror

Per-token competing-routes signature, capacity-titration threshold-collapse, volume-asymmetry between dense and dilute content, substrate-graded expertise reversal, and (v3) a depth-of-commitment bifurcation at the overload cliff observed on LLM substrates make the substrate-mechanical constraints empirically visible. The LLM evidence is positioned as mechanical mirror, not load-bearing: Paper 1 (substrate theory) and Paper 4B (substrates encode experience) carry the load as Zenodo-live empirical anchors; Paper 4 (LLM calibration) and Paper 13 (Operational Friction Theory) provide preliminary mechanistic support.

Three classroom failure modes from one mechanism

Three distinct classroom failure modes are derived from one substrate-mechanical constraint:

• Dump — raw quantity exceeds race-arbitration capacity.
• Dilute — correct information at insufficient race-opening density.
• Ambiguity-without-commit — multiple strategies sustained throughout without commit-pressure release.

The matched-friction principle (Paper 6) specifies the don't-over-explain corollary as upper-boundary matched-friction violation. Diagnostic protocols and intervention recipes are mode-specific.

Methodological commitments

Programmatic hypothesis. The substrate-level account is presented as a programmatic hypothesis, not a closed theory. Section 2.7 positions Friction Theory relative to predictive-processing (Friston, Clark), ACT-R (Anderson), and Bayesian cognitive science (Chater, Oaksford); commit-pressure under bounded race-architecture is identified as the load-bearing structural assumption that distinguishes the substrate-level account from these alternatives.

Implications. Section 8 includes:

• Curiosity gaps as engineered matched-friction on race-opening (sustain-pressure mechanism).
• Pre-flag bargain as reactance-bypass via voluntary commit.
• Meaning-field mis-diagnosis as dominant organisational learning failure.
• Formal-education abstraction-mismatch as structural feature of context-independent curriculum.
• Dunning-Kruger as a substrate-general default (v3) — confident-before-competent falls out of the architecture, modulated by a learned meta-confidence component rather than a special bias.

Four falsification conditions:

1. Substrate-mechanical signature on biological measurements.
2. Matched-friction window measurability.
3. Capacity-titration threshold-collapse.
4. Per-token competing-routes signature on biological substrates.

Connections to other papers in the series

• Paper 1 (Friction Theory) — the substrate-universal framework whose race-axioms (R1–R3) Paper 16 translates to learning didactics.
• Paper 4B (Substrates Encode Experience) — encoding-through-loading. Paper 16 derives the testing effect as a special case.
• Paper 6 core (Matched Friction) — the matched-friction-under-hysteresis schema. Paper 16's "don't-over-explain corollary" is the upper-boundary violation.
• Paper 4 (Same Content, Wider Track) — the empirical-calibration battery. Paper 16's matched-friction window appeals to Paper 4's eight axes as substrate evidence.
• Paper 13 (Operational Friction Theory) — race-opening, recursive resolution. Paper 16's three failure modes (dump/dilute/ambiguity) are operationally compatible with Paper 13's race-opening framing.
• Paper 0 (BFT) — the four-fields architecture. Paper 16's safety-field-prepotency derives from BFT's field-allocation account.

Read the paper

The full paper is on Zenodo (concept DOI 10.5281/zenodo.20416959):

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