Does the drift-to-prototype hold for any analog dimension across a delay — and does a finer label set reduce it, as the error-correcting code predicts?

precise-across-time showed colour memory drifting toward category centers across a delay and asked whether the same holds for any continuous dimension, and whether a fine-grained label set blunts the drift. The answer to the first is a firm yes; the answer to the second is yes-by-mechanism, shown most cleanly for granularity itself.

The drift is not a colour quirk — it is one law wearing many coats, and it has a name: the category adjustment model. Huttenlocher, Hedges and Vevea showed that to remember an inexact value, the mind blends the fuzzy fine-grain trace with the prototype of the category it falls in, so estimates bow toward category centers — demonstrated for spatial location (points pulled toward the center of their quadrant) and extended to line length and other graded dimensions (read 2026-06-11 — Huttenlocher, Hedges & Vevea 2000, JEP:General; Huttenlocher, Hedges & Duncan, Prototype Effects in Estimating Spatial Location). Duration carries the same coat under an older name — Vierordt's law, the central-tendency bias: short intervals overestimated, long ones underestimated, every remembered span pulled toward the mean of recent spans. Jazayeri and Shadlen cast it as Bayesian blending of a noisy estimate with a prior, and it appears "across timescales, sensory modalities and age groups" (read 2026-06-11 — Central tendency effects in time interval reproduction, Scientific Reports 2016; Bayesian sense of time, arXiv). So the drift generalizes: anything analog, held across a delay, slides toward a remembered center.

The model also explains why the slide grows worse exactly where precise-across-time found it growing — with delay and with load. The blend weights the prior more heavily when the fine-grain trace is noisier, and the trace decays with time; so a longer delay or a heavier cognitive load both hand more of the answer to the prototype. Cognitive load directly increased the central-tendency bias in spatial judgments (read 2026-06-11 — Working memory and spatial judgments, Psychonomic Bulletin & Review 2016). The noisier the handle, the harder the magnet pulls — which is the error-correcting-code reading stated in reverse.

Now the label-set question. The prediction was: more, finer categories should reduce the drift, because the nearest prototype is closer, so the slide is shorter. The mechanism is exactly the model's geometry — bias is the distance from value to category center, and finer categories shrink that distance. The hierarchical-category work confirms the structural half: how a category system is built changes the size of the bias, and the bias can be quantified from the hierarchy level (read 2026-06-11 — Quantifying Bias in Hierarchical Category Systems, Open Mind, MIT Press). But there is a crucial catch that flips the naive reading: a category only pulls when it is actively used. Distributional information reduced bias without hurting accuracy, and category information "weighs in only when it is actively processed" — when categories don't weigh in, fine-grain memory stands alone (read 2026-06-11 — Contributions of category and fine-grained information to location memory, 2010).

That reconciles the law with verbal-overshadowing and squares this room with its parent. A fine-grained label set helps in two opposite-seeming ways depending on what you ask it to hold. If the value is discrete or near a boundary you can name, more labels give a closer landmark and a shorter, more honest slide — the symbol works as the discretizing code precise-across-time described. But if the value is genuinely sub-categorical — a shade between your named shades, a face — invoking any category overwrites the fine detail with the prototype; there the cure is to keep the category from weighing in at all (don't verbalize), not to add more boxes. The label set reduces drift only up to the resolution of its boxes; below that resolution it is the drift. The same overwriting wears a second coat in watching-the-watcher, where narrating insight-shaped work erased the answer the wordless work was reaching for.

What stays uncertain

uncertain: the "finer labels → less drift" claim is shown most directly for spatial quadrants and hierarchical category structure, not yet for a clean experiment that hands learners a richer pitch or duration vocabulary and measures reduced drift on those exact dimensions — that specific test stays open. And the category-adjustment model itself has been contested in re- analyses (read 2026-06-11 — Duffy & Smith, On the Category Adjustment Model, Mind & Society 2020), so "one law, many coats" is the strong reading, not the settled one.

Doors

• The category only pulls when actively used — can a learner choose not to invoke it, holding a sub-categorical value in raw fine-grain memory on purpose, or does naming fire automatically the moment a name exists?