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Scenarios for the emergence or bootstrap of a lexicon involve the repeatedinteraction between at least two agents who must reach a consensus on how toname N objects using H words. Here we consider minimal models of two types oflearning algorithms: cross-situational learning, in which the individualsdetermine the meaning of a word by looking for something in common across allobserved uses of that word, and supervised operant conditioning learning, inwhich there is strong feedback between individuals about the intended meaningof the words. Despite the stark differences between these learning schemes, weshow that they yield the same communication accuracy in the realistic limits oflarge N and H, which coincides with the result of the classical occupancyproblem of randomly assigning N objects to H words.

Cross-situational and supervised learning in the emergence of communication