The founding deep-network critical-period paper validated its timing against animal data only, stating human clinical data was too sparse to regress a comparable curve

The paper that established critical periods in deep networks — Achille, Rovere & Soatto (2019) — fits its deep network's sensitivity-to-deficit curve against animal data: monocularly-deprived-kitten results (Olson & Freeman 1980; Giffin & Mitchell 1978) and macaque synaptic-density-over-age data (Rakic et al. 1986). It does not fit the curve against any human clinical dataset. The authors present this as a substitution forced by data availability rather than a modelling choice: "there is not enough data to confidently regress sensibility curves comparable to those obtained in DNNs. For this reason, in Figure 1 we compare the performance loss in a DNN trained in the presence of a cataract-like deficit with the results obtained from monocularly deprived kittens" (Appendix C).

Human amblyopia enters the paper only qualitatively, as motivating background citing clinical literature (von Noorden 1981; Taylor et al. 1979) for the point that treatment outcome depends on both the duration of the deficit and its age of onset. That qualitative dependence is never turned into a curve the DNN's own sensitivity profile is fit or compared against.

This scopes the vault's headline critical-period result. The match that claim-deep-nets-have-critical-learning-periods-timed-like-animals records is an animal match — the onset/length signature the net shares with the monocular-deprivation result (claim-monocular-deprivation-permanently-rewires-visual-cortex). The mechanism the net reproduces without biological hardware is treated in claim-critical-periods-arise-from-information-plasticity-not-biology. What this note fixes is the ceiling on how far the founding paper itself carried the claim toward human timing: not at all, and by explicit admission of missing data. Whether any later work has closed that gap is the open loop-closure thread question-deep-net-critical-period-predicts-human-amblyopia-timing, answered provisionally in claim-no-dnn-model-has-matched-human-critical-period-timing.