Human lexicons contain many different words that speakers can use to refer to the same object, e.g., "purple" or "magenta" for the same shade of color. On the one hand, studies on language use have explored how speakers adapt their referring expressions to successfully communicate in context, without focusing on properties of the lexical system. On the other hand, studies in language evolution have discussed how competing pressures for informativeness and simplicity shape lexical systems, without tackling in-context communication. We aim at bridging the gap between these traditions, and explore why a soft mapping between referents and words is a good solution for communication, by taking into account both in-context communication and the structure of the lexicon. We propose a simple measure of informativeness for words and lexical systems, grounded in a visual space, and analyze color naming data for English and Mandarin Chinese. We conclude that optimal lexical systems are those where multiple words can apply to the same referent, conveying different amounts of information. Such systems allow speakers to maximize communication accuracy and minimize the amount of information they convey when communicating about referents in contexts.

翻译：人类词汇库包含许多不同的词语，说话者可用以指称同一物体，例如用“紫色”或“品红色”指代同一色调的颜色。一方面，关于语言使用的研究探讨了说话者如何调整其指称表达以适应语境并实现成功交际，但未聚焦于词汇系统的特性。另一方面，语言演化研究讨论了信息量与简洁性之间的竞争压力如何塑造词汇系统，但未涉及语境中的实际交际。本研究旨在弥合这两种研究传统之间的鸿沟，通过同时考虑语境交际与词汇结构，探讨为何指称对象与词语之间的软映射是交际的有效解决方案。我们提出一种基于视觉空间的、针对词语及词汇系统的简易信息量度量方法，并分析了英语和汉语普通话的颜色命名数据。研究结论表明，最优的词汇系统应允许多个词语适用于同一指称对象，且能传递不同的信息量。此类系统使说话者能够在语境中交流指称对象时，最大化交际准确性并最小化所传递的信息量。