Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
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Large-scale analysis of global popular music shows uncorrelated melodic and rhythmic diversities, with only rhythm linked to ethnic and linguistic heterogeneity.
People judge copying AI-generated content as less wrong than copying human work because AI lacks moral patiency and humans claim more ownership of AI outputs.
citing papers explorer
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Human face perception reflects inverse-generative and naturalistic discriminative objectives
Human face perception aligns with neural networks trained on inverse-generative and naturalistic discriminative tasks, as these best predict human dissimilarity judgments on controversial and random face pairs.
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Do Melody and Rhythm Coevolve?
Large-scale analysis of global popular music shows uncorrelated melodic and rhythmic diversities, with only rhythm linked to ethnic and linguistic heterogeneity.
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Can AI be a moral victim? The role of moral patiency and ownership perceptions in ethical judgments of using AI-generated content
People judge copying AI-generated content as less wrong than copying human work because AI lacks moral patiency and humans claim more ownership of AI outputs.