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。

神经网络中的现场学习出现人类课程效果

Human Curriculum Effects Emerge with In-Context Learning in Neural  Networks

Cooperative communication plays a central role in theories of human
cognition, language, development, culture, and human-robot interaction. Prior
models of cooperative communication are algorithmic in nature and do not shed
light on why cooperation may yield effective belief transmission and what
limitations may arise due to differences between beliefs of agents. Through a
connection to the theory of optimal transport, we establishing a mathematical
framework for cooperative communication. We derive prior models as special
cases, statistical interpretations of belief transfer plans, and proofs of
robustness and instability. Computational simulations support and elaborate our
theoretical results, and demonstrate fit to human behavior. The results show
that cooperative communication provably enables effective, robust belief
transmission which is required to explain feats of human learning and improve
human-machine interaction.

合作交流在人类认知、语言、发展、文化和人机交互理论中发挥着核心作用，本文提出了一种采用最优传输理论建立数学框架的新模型，证明了合作交流能够促进有效、鲁棒的信念传播，从而解释了人类学习的奇迹，并改进了人机交互。

合作通信的数学理论

A mathematical theory of cooperative communication

In the study of human learning, there is broad evidence that our ability to
retain information improves with repeated exposure and decays with delay since
last exposure. This plays a crucial role in the design of educational software,
leading to a trade-off between teaching new material and reviewing what has
already been taught. A common way to balance this trade-off is spaced
repetition, which uses periodic review of content to improve long-term
retention. Though spaced repetition is widely used in practice, e.g., in
electronic flashcard software, there is little formal understanding of the
design of these systems. Our paper addresses this gap in three ways. First, we
mine log data from spaced repetition software to establish the functional
dependence of retention on reinforcement and delay. Second, we use this memory
model to develop a stochastic model for spaced repetition systems. We propose a
queueing network model of the Leitner system for reviewing flashcards, along
with a heuristic approximation that admits a tractable optimization problem for
review scheduling. Finally, we empirically evaluate our queueing model through
a Mechanical Turk experiment, verifying a key qualitative prediction of our
model: the existence of a sharp phase transition in learning outcomes upon
increasing the rate of new item introductions.

使用间隔重复系统的日志数据，针对人类学习中重复学习的作用，在 Leitner 系统中优化复习进度，通过机械土耳其实验验证新物品引入的速率对学习结果的影响等方面进行研究。