Associative learning--forming links between co-occurring items--is fundamental to human cognition, reshaping internal representations in complex ways. Testing hypotheses on how representational changes occur in biological systems is challenging, but large language models (LLMs) offer a scalable alternative. Building on LLMs' in-context learning, we adapt a cognitive neuroscience associative learning paradigm and investigate how representations evolve across six models. Our initial findings reveal a non-monotonic pattern consistent with the Non-Monotonic Plasticity Hypothesis, with moderately similar items differentiating after learning. Leveraging the controllability of LLMs, we further show that this differentiation is modulated by the overlap of associated items with the broader vocabulary--a factor we term vocabulary interference, capturing how new associations compete with prior knowledge. We find that higher vocabulary interference amplifies differentiation, suggesting that representational change is influenced by both item similarity and global competition. Our findings position LLMs not only as powerful tools for studying representational dynamics in human-like learning systems, but also as accessible and general computational models for generating new hypotheses about the principles underlying memory reorganization in the brain.

翻译：联想学习——在共现项目之间建立联系——是人类认知的基础，它以复杂的方式重塑内部表征。在生物系统中检验表征变化如何发生的假设具有挑战性，但大型语言模型（LLMs）提供了一种可扩展的替代方案。基于LLMs的上下文学习能力，我们改编了一个认知神经科学的联想学习范式，并研究了六个模型中表征的演变过程。我们的初步发现揭示了一种与非单调可塑性假设一致的非单调模式，即中等相似的项目在学习后发生分化。利用LLMs的可控性，我们进一步表明这种分化受到关联项目与更广泛词汇表重叠程度的调节——我们称之为词汇干扰的因素，它捕捉了新关联如何与先验知识竞争。我们发现更高的词汇干扰会放大分化，这表明表征变化既受项目相似性影响，也受全局竞争影响。我们的研究结果将LLMs定位为不仅可用于研究类人学习系统中表征动态的强大工具，而且可作为可访问的通用计算模型，用于生成关于大脑记忆重组潜在原理的新假设。