The spacing effect can emerge unbidden from gradient descent: self-lengthening review intervals appeared in an MLP no one designed for them

The spacing effect — that memory is best maintained by reviews at expanding rather than fixed intervals — is a 140-year-old finding from Ebbinghaus's 1885 self-experiments and a staple of spaced-repetition software. Kline (2025) reports it re-appearing inside a neural network without being built in. Working with the same MLP-on-MNIST forgetting setup that produces a graceful power-law decay for a dropped class (claim-neural-nets-forget-along-human-like-power-law-curve), he triggered a "review" — oversampling the dropped class 8 — whenever its recall probability dipped to 80% of its peak. Across 100 epochs this required 5 reviews, and the gaps between them stretched on their own: "the intervals between reviews lengthened progressively—from about 4 epochs before the first review, to 10 before the second and third, and 31 before the fourth." By the fifth review, peak recall exceeded 0.45 — more than double the pre-review low near 0.18.

The point is that the expanding schedule is not imposed. The review policy here fixes only a recall threshold (80% of peak); the widening intervals are an emergent consequence of the network relearning the class more durably each time, so that recall takes progressively longer to decay back to threshold. Ebbinghaus's expanding-schedule optimum thus falls out of gradient descent as a side effect of the underlying forgetting dynamics, rather than as a designed-in curriculum.

This is the constructive counterpart to the vault's decay-side memory notes: it is not only that machine memory fades along a human-like curve, but that a human-like maintenance strategy is recoverable from the same dynamics. It extends the neuro-AI-parallel cluster (claim-critical-periods-arise-from-information-plasticity-not-biology, claim-deep-nets-have-critical-learning-periods-timed-like-animals) — a plain optimizer reproducing a phenomenon usually credited to cognition — and connects to the engineering side of AI memory, where forgetting-curve-inspired replay is an active design target (claim-agent-memory-field-shifted-storage-to-experience-2026).