Using generative artificial intelligence to test hypotheses about animal signal evolution: A case study in an ornamented fish

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paper

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HéjMilRenMen

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UniCenThi

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CCSD

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The sensory drive hypothesis of animal signal evolution suggests that animal communication signals evolve in response to environmental pressures. While classical approaches to testing this hypothesis focus on one aspect of the signal, deep learning techniques like generative models can create and manipulate stimuli without targeting a specific feature. Here, we used a technique called style transfer to experimentally test preferences for colour patterns in a fish. We manipulated how similar or dissimilar male body patterns were to their habitats using the Neural Style Transfer (NST) algorithm. We predicted that males whose body patterns are similar to their habitats are easier to process and thus preferred by conspecifics. Our findings suggest that both males and females tend to be sensitive to habitat congruence in their preferences, but to different extents, requiring additional investigation. Nonetheless, this study demonstrates the potential of deep learning techniques in testing hypotheses about animal communication signals.

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