Retrieval check answer. The algorithmic level is held fixed (same search algorithm, minimax with alpha-beta pruning); the implementational level varies (phone hardware vs. a data-center cluster). Marr’s whole point: one algorithm can run on many substrates, so a claim about which algorithm is used says nothing about, and is unconstrained by, which hardware it happens to run on.
Why Schedule 2 is the more valuable review
Right after learning something, activation is still high — the trace hasn’t decayed much (in lesson 15’s terms, t is small, so t^(−d) is close to its maximum). Reviewing it 1 hour later hits the fact while it’s still almost fully active: the “retrieval” barely has to reconstruct anything, because the trace never really left. That’s functionally closer to lesson 16’s recognition — a cheap lookup of something still sitting near the surface — even though nothing is literally on screen. It adds a use-event, technically, but a shallow one.
Reviewing 3 days later hits the fact after real decay has set in: the trace has weakened enough that reconstructing it takes genuine effort, closer to lesson 16’s recall. That’s the kind of use lesson 16 argued does the real work of strengthening a retrieval pathway, not just re-exposing content.
The design rule this implies: a review only earns its full value as a use-event if it happens after the previous trace has decayed enough that retrieving it again requires real reconstruction — not merely “after some fixed clock interval.” Reviewing too soon (Schedule 1) wastes the review on a trace that hasn’t forgotten anything yet; reviewing far too late risks outright retrieval failure (activation has dropped so low the attempt fails rather than succeeds, which teaches nothing and can be discouraging). The right target is the region in between: spaced just far enough that recall requires genuine effort and typically still succeeds — which is why effective spacing schedules lengthen the gap between reviews over time (each successful review resets the clock on a now-stronger, slower-decaying trace, so the next gap that produces “genuine effort, likely success” is longer than the last).
Interleaving, briefly
The same “genuine effort beats easy fluency” logic drives interleaving. Practicing several related skills mixed together (rather than one skill in an isolated block) is harder in the moment specifically because you can’t coast on “I already know which technique applies here, since I’ve been doing this one for the last ten problems” — you have to actually retrieve which technique fits this problem, every time. That’s an extra retrieval demand blocked practice never imposes, and it’s exactly the skill (identifying which technique a novel, unlabeled problem calls for) that later transfers to real, un-labeled problems.
This path’s own design, mapped: the routine’s buffer policy — keeping only a small number of unsolved lessons ahead of you, delivered over real days rather than dumped at once — is a spacing mechanism, aimed at the region between “too soon” and “too late” this lesson just derived. Interleaving four tracks (fp, bayes, cog, ml) in the same sitting, rather than finishing one track’s full spine first, is the domain-level version of Rohrer & Taylor’s mixed problem sets: it forces you to retrieve which domain’s thinking a given puzzle calls for, not just execute a technique you were already told to use.
Where this goes: back to blackboard architecture, sharpened by one changed requirement — the same “one factor decides everything” move this lesson just made for spacing, applied to when a pipeline stops being adequate.