It has been argued that semantic systems reflect pressure for efficiency, and a current debate concerns the cultural evolutionary process that produces this pattern. We consider efficiency as instantiated in the Information Bottleneck (IB) principle, and a model of cultural evolution that combines iterated learning and communication. We show that this model, instantiated in neural networks, converges to color naming systems that are efficient in the IB sense and similar to human color naming systems. We also show that some other proposals such as iterated learning alone, communication alone, or the greater learnability of convex categories, do not yield the same outcome as clearly. We conclude that the combination of iterated learning and communication provides a plausible means by which human semantic systems become efficient.

翻译：语义系统被认为反映了对效率的追求，当前关于产生这种模式的文化演化过程存在争议。我们将效率视为信息瓶颈原则的体现，并提出一种结合迭代学习与沟通的文化演化模型。研究表明，该模型在神经网络中实现后，收敛于在信息瓶颈意义上高效且与人类颜色命名系统相似的颜色命名系统。我们还证明，其他方案（如单独迭代学习、单独沟通，或凸类别的更高可学习性）无法同样明确地产生相同结果。我们认为，迭代学习与沟通的结合为人类语义系统实现高效性提供了一种合理的途径。