Human learning is sensitive to rule-like structure and the curriculum of
examples used for training. In tasks governed by succinct rules, learning is
more robust when related examples are blocked across trials, but in the absence
of such rules, interleaving is more effective. To date, no neural model has
simultaneously captured these seemingly contradictory effects. Here we show
that this same tradeoff spontaneously emerges with "in-context learning" (ICL)
both in neural networks trained with metalearning and in large language models
(LLMs). ICL is the ability to learn new tasks "in context" - without weight
changes - via an inner-loop algorithm implemented in activation dynamics.
Experiments with pretrained LLMs and metalearning transformers show that ICL
exhibits the blocking advantage demonstrated in humans on a task involving
rule-like structure, and conversely, that concurrent in-weight learning
reproduces the interleaving advantage observed in humans on tasks lacking such
structure.

通过使用元学习进行训练的神经网络和大型语言模型，我们展示了 ' 在上下文学习 '（ICL）在涉及类似规则结构的任务中表现出了人类所示的阻塞优势，反之，在缺乏此类结构的任务上，并发的权重学习产生了人类所观察到的交错优势。