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Comparing Activation Typicality and Sparsity in a Deep CNN to Predict Facial Beauty

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Abstract

Processing fluency, which describes the subjective sensation of ease with which information is processed by the sensory systems and the brain, has become one of the most popular explanations of aesthetic appreciation and beauty. Two metrics have recently been proposed to model fluency: the sparsity of neuronal activation, which describes the concentration of activity in a subset of neurons, and the statistical typicality of activations, which describes how well the encoding of a stimulus matches a reference representation of stimuli of the category to which it belongs. Using convolutional neural networks (CNNs) as a model for the human visual system, this study compares the ability of these metrics to explain variation in facial attractiveness. Our findings show that the sparsity of neuronal activations is a more robust predictor of facial attractiveness than statistical typicality. Refining the reference representation to a single ethnicity or gender does not increase the explanatory power of statistical typicality. However, statistical typicality and sparsity predict facial beauty based on different layers of the CNNs, suggesting that they describe different neural mechanisms underlying fluency.

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Data Availability

Data from the Chicago Face Dataset: https://www.chicagofaces.org/

Data from the FairFace dataset: https://github.com/joojs/fairface

Code Availability

Data from Chicago Face Dataset: https://www.chicagofaces.org/ Data from FairFace dataset: https://github.com/joojs/fairface.

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Funding

This study was funded by the Agence Nationale de la Recherche (ANR-20-CE02-0005–01), the National Science Foundation (NSF IOS 2026334), and by the Mission for Interdisciplinarity of the French National Center for Scientific Research (Programme Interne Blanc CNRS MITI 2023.1 – DEEPCOM project).

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Author notes

  1. William Puech and Julien P. Renoult contributed equally to this work.

Authors and Affiliations

  1. CEFE, Univ Montpellier, CNRS, EPHE, IRD, Montpellier, France

    Sonia Tieo, Nicolas Dibot & Julien P. Renoult

  2. Terakalis, Montpellier, France

    Melvin Bardin

  3. CERCO, UMR5549, Centre de Recherche Cerveau Et Cognition, Toulouse, France

    Roland Bertin-Johannet

  4. Department of Biological Sciences, University of Maryland, Baltimore County, Baltimore, MD, USA

    Tamra C. Mendelson

  5. LIRMM, Univ. Montpellier, CNRS, Montpellier, France

    William Puech

Authors
  1. Sonia Tieo
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  2. Melvin Bardin
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  3. Roland Bertin-Johannet
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  4. Nicolas Dibot
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  5. Tamra C. Mendelson
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  6. William Puech
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  7. Julien P. Renoult
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Contributions

Sonia Tieo: Writing – Original draft, Methodology, Supervision, Formal Analysis, Software. Tamra C.Mendelson: Conceptualization, Supervision, Funding Acquisition, Writing – Review & Editing. Julien P. Renoult: Conceptualization, Supervision, Funding Acquisition, Writing – Review & Editing. William Puech: Conceptualization, Supervision, Funding Acquisition, Writing – Review & Editing. Melvin Bardin: Methodology, Formal Analysis, Software Roland Bertin-Johannet: Methodology, Formal Analysis, Software Nicolas Dibot: Methodology, Supervision.

Corresponding authors

Correspondence to Sonia Tieo or Julien P. Renoult.

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Tieo, S., Bardin, M., Bertin-Johannet, R. et al. Comparing Activation Typicality and Sparsity in a Deep CNN to Predict Facial Beauty. Comput Brain Behav 8, 249–261 (2025). https://doi.org/10.1007/s42113-024-00231-7

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