Mount Stupid in the machine: how evidence competition explains the Dunning-Kruger curve in a language model

The Dunning-Kruger “valley of despair” is, in a language model, a near-tie of about 0.4 nats in the substrate’s balance-of-evidence margin that greedy decoding renders as full stated confidence — and a validated log-probability proxy reads the latent decision variable directly, beating a fitted scalar-surprisal model (ΔAIC +54, pooled n = 749), including a matched-surprisal dissociation (Δ = +1.44) to which the field’s scalar measure is blind. The Dunning-Kruger pattern is among the most cited and most contested findings in metacognition, and the dispute is largely about measurement: the load-bearing quantity — how strongly a person’s competing answers compete before commitment — is never observed directly, but reconstructed from confidence and accuracy scores entangled with regression-to-the-mean and scale-use. This paper changes substrate. In a large language model that same quantity is read directly from the next-token probability distribution. The reading is first validated as a proxy for the sequential-sampling (race / leaky-competing-accumulator) decision variable; the proxy is then used as a model organism for the Dunning-Kruger curve, yielding a partial reproduction with principled divergences. The confident-before- competent rise, the recognition near-tie, and the recalibration slope are architectural (and the rise plus recognition replicate on a second, structurally different hidden-rule task). Novice humility is absent under forced commitment but restored by an abstention channel; the affective valley is absent in behaviour yet present in the substrate margin. The paper thereby localises which phases of the human curve are properties of bounded competing-route computation and which require the biological substrate, and closes with the preregistered human study that would test the analogy directly. Companion papers (Friction Theory series): Behavioural Friction Theory (Paper 0); Friction as the Cost of Probabilistic Computation (Paper 1); Logic as Reactance (Paper 14); The Physics of Learning (Paper 16); Compete, Don’t Erase (knowledge-editing erase-vs-mask); and An LLM as a Controllable, Fully-Inspectable Model of Measurement. Series position. Paper 21 in the Friction Theory paper-series. Data, prompts, scripts, and per-item outputs are released with the paper (see the Data and code availability section).