Cultural evolution via iterated learning and communication explains efficient color naming systems
This addresses the problem of explaining the cultural evolution of semantic systems for linguists and cognitive scientists, but it is incremental as it builds on existing debates about efficiency.
The study tackled how human color naming systems achieve efficiency by modeling cultural evolution through iterated learning and communication, showing that this combination converges to systems similar to humans and efficient under the Information Bottleneck principle, unlike other proposals like iterated learning or communication alone.
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.