Proceedings of the Annual Meeting of the Cognitive Science Society

As observed in the World Color Survey (WCS), some univer-sal properties can be identified in color naming schemes overa large number of languages. For example, Regier, Kay, andKhetrapal (2007) and Regier, Kemp, and Kay (2015); Gib-son et al. (2017) recently explained these universal patterns interms of near optimal color partitions and information theoreticmeasures of efficiency of communication. Here, we introducea computational learning framework with multi-agent systemstrained by reinforcement learning to investigate these universalproperties. We compare the results with Regier et al. (2007,2015) and show that our model achieves excellent quantitativeagreement. This work introduces a multi-agent reinforcementlearning framework as a powerful and versatile tool to investi-gate such semantic universals in many domains and contributesignificantly to central questions in cognitive science.