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The development and evolution of specialized face learning in paper wasps

Tibbetts, Elizabeth A., Den Uyl, John, Dwortz, Madeleine, McLean, Cailin
Animal behaviour 2019 v.147 pp. 1-7
Polistes dominula, Polistes fuscatus, Polistes metricus, animal behavior, animals, cognition, environmental impact, evolution, face, learning, paper wasps, social benefit
Some animals are thought to exhibit cognitive specialization, as they have specialized cognitive modules that solve specific social or ecological problems instead of one general-purpose mechanism that addresses diverse problems. Although there are many examples of specialized cognition, little is known about whether specialization develops through experience or is produced by innate, species-specific differences. Previous work has shown that Polistes fuscatus wasps use face recognition to individually identify other wasps and that P. fuscatus are specialized for learning conspecific faces. Here, we test how experimentally altering face experience in three Polistes species influences the development of face specialization. We show face learning is influenced by both experience and innate, species-specific differences. In P. fuscatus, experience with conspecific faces is not required for the development of face specialization. In two related Polistes species that naturally lack individual face recognition, Polistes metricus and Polistes dominula, experience has different effects on specialization. Polistes metricus, a close relative of P. fuscatus, develops face specialization with experience. However, P. dominula, a more distant relative, uses general pattern recognition to learn faces regardless of experience. Therefore, some species have innate mechanistic architecture that facilitates the development of face specialization, while other species do not. These results suggest that selection shapes animal minds in a modular manner. The capacity for specialized cognitive skills evolves in response to specific ecological or social demands, such as social benefits associated with accurate individual face recognition.