AI RESEARCH
Modeling Human-Like Color Naming Behavior in Context
arXiv CS.CL
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ArXi:2604.25674v1 Announce Type: new 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, 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.