Cultural evolution via iterated learning and communication explains efficient color naming systems

TL;DR

This paper demonstrates that a combined model of iterated learning and communication, implemented via neural networks, explains how human color naming systems evolve to be efficient according to the Information Bottleneck principle.

Contribution

It introduces a neural network model combining iterated learning and communication that reproduces efficient human-like color naming systems, advancing understanding of cultural evolution.

Findings

The combined model converges to efficient color naming systems similar to humans.

Iterated learning or communication alone do not produce the same efficiency.

The model aligns with the Information Bottleneck principle in semantic systems.

Abstract

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.