Understanding The Role of Perception in the Evolution of Human Language


We propose a flexible modeling framework for studying the role of perception in language learning and language evolution. This is achieved by augmenting some novel and some existing evolutionary signaling game models with existing techniques in machine learning and cognitive science. The result is a ``grounded'' signaling game in which agents extract relevant information from their environment via a cognitive processing mechanism, then learn to communicate that information with each other. The choice of cognitive processing mechanism is left as a free parameter, allowing the model to be tailored to a wide variety of problems. We present results from simulations using both a Bayesian perception model and a neural network based perception model, which demonstrate how perception can ``pre-process'' environmental data in a way that is well suited for communication. Lastly, we discuss how the model can be extended to study other roles that perception may play in language learning.

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