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ABSTRACT The world around us is full of beauty. Explaining a sense of the beautiful has beguiled philosophers and artists for millennia, but scientists have also pondered beauty, most notably Darwin, who used beauty to describe sexual ornaments that he argued were the subject of female mate choice. In doing so, he ascribed a ‘sense of the beautiful’ to non‐human animals. Darwin's ideas about mate choice and beauty were not widely accepted, however. Humans may experience beauty, but assuming the same about other animals risks anthropomorphism: we might find the tail of the peacock to be beautiful, but there is no reason to believe that peahens do. Moreover, mate choice, resurrected as an object of serious study in the 1970s, simply requires attraction, not necessarily beauty. However, recent advances in psychology and cognitive neuroscience are providing a new, mechanistic framework for beauty. Here we take these findings and apply them to evolutionary biology. First, we review progress in human empirical aesthetics to provide a biological definition of beauty. Central to this definition is the discovery that merely processing information can provide hedonic reward. As such, we propose thatbeauty is the pleasure of fluent information processing, independent of the function or consummatory reward provided by the stimulus. We develop this definition in the context of three key attributes of beauty (pleasure, interaction, and disinterestedness) and the psychological distinction between ‘wanting’ and ‘liking’. Second, we show how beauty provides a new, proximate approach for studying the evolution of sexual signalling that can help us resolve some key problems, such as how mating biases evolve. We also situate beauty within a more general framework for the evolution of animal signals, suggesting that beauty may apply not only to sexual ornaments, but also to traits as diverse as aposematic signals and camouflage. Third, we outline a variety of experimental approaches to test whether animal signals are beautiful to their intended receivers, including tests of fluency and hedonic impact using behavioural and neurological approaches.
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Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics
Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty (‘feature-based aesthetics’), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver’s brain (‘processing-based aesthetics’), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.
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- Award ID(s):
- 2026334
- PAR ID:
- 10536444
- Editor(s):
- Fleming, Roland W
- Publisher / Repository:
- PLoS computational biology
- Date Published:
- Journal Name:
- PLOS Computational Biology
- Volume:
- 19
- Issue:
- 12
- ISSN:
- 1553-7358
- Page Range / eLocation ID:
- e1011703
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1371/journal.pcbi.1011703
