Vision-Language Models Assimilate Where Humans Contrast: A Cross-Architecture Signature of Contextual Computation

Which “cognitive biases” and “perceptual illusions” are necessary signatures of a bounded decision architecture, and which are mere artefacts? Behavioural data alone cannot say, because the latent decision variable must be inferred from aggregated choices. The per-token competing-routes (log-probability) substrate of a language model makes that latent observable, and can therefore act as an instrument that sorts a candidate phenomenon into phantom (an observer-aggregation artefact), architecture-forced (a real bounded-commitment signature), or substrate accident. This paper states that sorting criterion and demonstrates it in perception, using vision-language models (VLMs) as a no-retina visual substrate. With a gate-controlled, prior-orthogonalised forced-choice readout, classical inferential illusions recur as contextual modulation with a measurable commitment signature — but with a substrate-specific sign: VLMs modulate a target toward its surround (assimilation) where human perception modulates it away (contrast). The effect is a graded dose-response that replicates across three architecturally distinct vision encoders (a native dynamic-resolution ViT, a SigLIP encoder, and a CLIP encoder) and across two perceptual dimensions — size (Ebbinghaus, Delboeuf) and brightness (simultaneous-contrast) — with every one of nine dose-response slopes’ 95% confidence interval excluding zero. The opposite sign is strong evidence against the simplest account — that VLMs imitate human illusion reports absorbed from text — and is consistent with a systematically different contextual computation; whether that sign is forced by the architecture or learned from training-data statistics is left as the live open question, with the cross-corpus convergence (the same sign across three models trained on different data) weighing against a dataset-specific account. Companion papers in the series develop the cognitive and self-knowledge faces of the same “biases-as-architecture” claim, the social-friction worked cases, and the competing-routes measurement-model programme. Prepared for submission to Transactions on Machine Learning Research (TMLR). Data and code. The stimulus generators, evaluation notebooks, and per-token log-probability outputs are available from the author.