Large Language Models as Model Organisms for Human Associative Learning
Camila Kolling, Vy Vo, Mariya Toneva
Associative learning--forming links between co-occurring items--is fundamental to human cognition, reshaping internal representations in complex ways. Competing hypotheses propose that associative learning in the brain either integrates similar items to promote generalization, differentiates them to minimize interference, or--according to a more intricate theory--follows a non-monotonic pattern where moderately similar items differentiate while highly similar or dissimilar items integrate or remain stable. Testing these hypotheses in biological systems is challenging, but large language models (LLMs) offer a scalable alternative. Leveraging LLMs' in-context learning (ICL), we adapt a cognitive neuroscience associative learning paradigm and investigate representational changes across six LLMs. Our findings align best with the Non-Monotonic Plasticity Hypothesis (NMPH), showing that moderately similar items differentiate after learning.However, we find that similarity to the model’s broader prior knowledge plays an important role by amplifying differentiation. 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.