Modeling Human-Like Color Naming Behavior in Context
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 ...
| Authors | Yuqing Zhang, Ecesu Ürker, Tessa Verhoef et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 April 2026 |
| Last updated at source | 8 May 2026 |
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