Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning.

ABSTRACT: Human learning embodies a striking duality: Sometimes, we can rapidly infer and compose logical rules, benefiting from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved. Influential psychological theories explain this seemingly conflicting behavioral evidence by positing two qualitatively different learning systems-one for rapid, rule-based inferences (e.g., in working memory) and another for slow, incremental adaptation (e.g., in long-term and procedural memory). It remains unclear how to reconcile such theories with neural networks, which learn via incremental weight updates and are thus a natural model for the latter, but are not obviously compatible wit

SUBMITTER: Russin J 

PROVIDER: S-EPMC12415290 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature