Correct: ACT-R maintains a separate declarative memory with graded, activation-based retrieval; SOAR compiles experience directly into new all-or-nothing production rules, with no separate declarative store in the classic architecture.
Both other mechanism-comparison options are wrong on the facts: SOAR clearly does learn (chunking is its learning mechanism), so “only SOAR learns” is false, and chunking and base-level activation are not the same thing — one produces a new discrete rule, the other continuously reweights an existing fact’s retrievability. “ACT-R only models procedural skill” is backwards, if anything — ACT-R is unusual precisely for modeling both declarative and procedural memory as separate, interacting systems.
Why this is a real design bet, not a footnote. Committing to “only production rules, nothing else” (SOAR) versus “facts and rules are genuinely different kinds of memory” (ACT-R) isn’t a style choice — it’s a claim about the mind with testable consequences. ACT-R’s bet lets it predict things SOAR’s architecture doesn’t naturally produce: partial, graded recall (a name on the tip of your tongue — retrievable, but weakly, exactly what lesson 10’s negative-but-not-infinitely-negative activation captured), and forgetting that’s continuous rather than all-or-nothing. SOAR’s bet predicts something different: skills that are either fully automatic (chunked, instant) or fully effortful (not yet chunked, requires full deliberate subgoal search) — a much sharper line than ACT-R’s smooth activation curve, and a genuinely different empirical claim about how skill acquisition should feel and time out as it develops.
Neither architecture is simply “more right.” Chunking’s crisp automatization story fits skill-acquisition data (a novice driver’s effortful gear-shifting becoming instant reflex) better than a smoothly graded activation curve would predict for that kind of case. ACT-R’s graded activation fits partial/uncertain recall (the tip-of-the-tongue state, or confidently misremembering a barely-active fact) that a strictly binary “chunked or not” story struggles to represent at all. Real cognition seems to need both flavors of forgetting-and-remembering somewhere — which is a live, unresolved argument between the two research communities, not settled by either architecture alone.
In a modern LLM agent harness, chunking rhymes with caching a resolved sub-task’s answer as a reusable fixed procedure (a memoized tool call, a saved successful plan) so the next occurrence skips straight to the cached result — while a separate, editable memory store the agent can also consult, reason over, and update mid-task (not baked into fixed behavior) is closer to ACT-R’s declarative side. Harnesses that only cache-and-replay lean SOAR; harnesses with a queryable memory store lean ACT-R — most real systems, tellingly, end up wanting both.
Where this goes: last lesson in this stage steps back from either architecture’s internals to ask the harder question — how well do either of these detailed mechanisms actually predict real human timing and error data, and where do they fall short?