TL;DR
This paper demonstrates that large language models can simulate human associative learning, revealing how representations change with learning and interference, thus serving as models for studying memory reorganization.
Contribution
It introduces a novel application of LLMs to model associative learning dynamics and identifies factors influencing representational differentiation, such as vocabulary interference.
Findings
Non-monotonic pattern of representation change consistent with hypotheses
Vocabulary interference modulates the degree of differentiation
LLMs can serve as models for studying human memory reorganization
Abstract
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…
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