Organisms can learn by individual experience to recognize relevant stimuli in the environment or they can genetically inherit this ability from their parents. Here, we ask how these two modes of acquisition affect signal evolution, focusing in particular on the exaggeration and cost of signals. We argue first, that faster learning by individual receivers cannot be a driving force for the evolution of exaggerated and costly signals unless signal senders are related or the same receiver and sender meet repeatedly. We argue instead that biases in receivers’ recognition mechanisms can promote the evolution of costly exaggeration in signals. We provide support for this hypothesis by simulating coevolution between senders and receivers, using artificial neural networks as a model of receivers’ recognition mechanisms. We analyse the joint effects of receiver biases, signal cost and mode of acquisition, investigating the circumstances under which learned recognition gives rise to more exaggerated signals than inherited recognition. We conclude the paper by discussing the relevance of our results to a number of biological scenarios
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