让机器学会像人一样给颜色命名，提升命名系统的几何合理性。
Modeling Human-Like Color Naming Behavior in Context
- 通过数据增广和多听者强化学习，模拟人类颜色命名机制。
- 适度数据增强与多听者互动使颜色分类更接近人类的凸区域特征。
- 适合研究语言演化、认知建模与人机交互的学者参考。
通过相互作用的神经代理模拟学习与交际压力，计算系统中人类式词汇的涌现已取得进展。NeLLCom-Lex框架（Zhang等，2025）利用人类数据监督学习和参照游戏中的强化学习，使神经代理发展出实用的颜色命名行为和人类类似的词汇系统。然而，生成的词汇系统在颜色空间中表现出高度非凸区域，与人类类别典型的凸性相悖。为此，本文引入两个改进：在监督学习中对稀有颜色词进行上采样，以及采用多听者强化学习交互，并使用凸性度量量化几何一致性。结果表明，上采样提升了词汇多样性与系统信息量，多听者设置促进了更凸的颜色类别。适度上采样与多听者结合可生成最接近人类系统的词汇体系。
原文摘要 · Abstract (English)
Modeling the emergence of human-like lexicons in computational systems has advanced through the use of interacting neural agents, which simulate both learning and communicative pressures. The NeLLCom-Lex framework (Zhang et al., 2025) allows neural agents to develop pragmatic color naming behavior and human-like lexicons through supervised learning (SL) from human data and reinforcement learning (RL) in referential games. Despite these successes, the lexicons that emerge diverge systematically from human color categories, producing highly non-convex regions in color space, which contrast with the convexity typical of human categories. To address this, we introduce two factors, upsampling rare color terms during SL and multi-listener RL interactions, and adopt a convexity measure to quantify geometric coherence. We find that upsampling improves lexical diversity and system-level informativeness of the color lexicon, while many-listener setups promote more convex color categories. The combination of moderate upsampling and multiple listeners produces lexicons most similar to human systems.
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