Proceedings of the Annual Meeting of the Cognitive Science Society

In this paper, we propose a flexible modeling framework forstudying the role of perception in language learning and lan-guage evolution. This is achieved by augmenting some noveland some existing evolutionary signaling game models withexisting techniques in machine learning and cognitive science.The result is a “grounded” signaling game in which agentsmust extract relevant information from their environment viaa cognitive processing mechanism, then learn to communi-cate that information with each other. The choice of cogni-tive processing mechanism is left as a free parameter, allow-ing the model to be tailored to a wide variety of problemsand tasks. We present results from simulations using both aBayesian perception model and a neural network based per-ception model, which demonstrate how perception can “pre-process” environmental data in a way that is well suited forcommunication. Lastly, we discuss how the model can be ex-tended to study other roles that perception may play in lan-guage learning.