Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning - Jacob Russin; Ellie Pavlick; Michael J. Frank (Proceedings of the National Academy of Sciences, 2025)
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 with the former. However, recent evidence suggests that metalearning neural networks and large language models are capable of in-context learning (ICL)—the ability to flexibly infer the structure of a new task from a few examples. In contrast to standard in-weight learning (IWL), which is analogous to synaptic change, ICL is more naturally linked to activation-based dynamics thought to underlie working memory in humans. Here, we show that the interplay between ICL and IWL naturally ties together a broad range of learning phenomena observed in humans, including curriculum effects on category-learning tasks, compositionality, and a trade-off between flexibility and retention in brain and behavior. Our work shows how emergent ICL can equip neural networks with fundamentally different learning properties that can coexist with their native IWL, thus offering an integrative perspective on dual-process theories of human cognition.