Biological bases of beauty revisited

The effect of symmetry, averageness, and sexual dimorphism on female facial attractiveness

Alex L. Jones*, Bastian Jaeger

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.
Original languageEnglish
Article number279
Number of pages25
JournalSymmetry
Volume11
Issue number2
DOIs
Publication statusPublished - 2019

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Human engineering
Learning algorithms
Learning systems
Skin
Textures
Symmetry
Face
Random Forest
Human Factors
Texture
Manipulation
Learning Algorithm
Machine Learning
Quantify
Paradigm
Predict
Decrease
Methodology
Prediction
Alternatives

Keywords

  • APPARENT HEALTH
  • DEVELOPMENTAL STABILITY
  • FACES
  • FLUCTUATING ASYMMETRY
  • HUMAN PREFERENCES
  • MATE-CHOICE
  • PERCEPTION
  • REDNESS INCREASES
  • SHAPE CUES
  • SKIN COLOR
  • attractiveness
  • averageness
  • dimorphism
  • faces
  • machine learning
  • symmetry

Cite this

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title = "Biological bases of beauty revisited: The effect of symmetry, averageness, and sexual dimorphism on female facial attractiveness",
abstract = "The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.",
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author = "Jones, {Alex L.} and Bastian Jaeger",
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}

Biological bases of beauty revisited : The effect of symmetry, averageness, and sexual dimorphism on female facial attractiveness. / Jones, Alex L.; Jaeger, Bastian.

In: Symmetry, Vol. 11, No. 2, 279, 2019.

Research output: Contribution to journalArticleScientificpeer-review

TY - JOUR

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AU - Jaeger, Bastian

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N2 - The factors influencing human female facial attractiveness—symmetry, averageness, and sexual dimorphism—have been extensively studied. However, recent studies, using improved methodologies, have called into question their evolutionary utility and links with life history. The current studies use a range of approaches to quantify how important these factors actually are in perceiving attractiveness, through the use of novel statistical analyses and by addressing methodological weaknesses in the literature. Study One examines how manipulations of symmetry, averageness, femininity, and masculinity affect attractiveness using a two-alternative forced choice task, finding that increased masculinity and also femininity decrease attractiveness, compared to unmanipulated faces. Symmetry and averageness yielded a small and large effect, respectively. Study Two utilises a naturalistic ratings paradigm, finding similar effects of averageness and masculinity as Study One but no effects of symmetry and femininity on attractiveness. Study Three applies geometric face measurements of the factors and a random forest machine learning algorithm to predict perceived attractiveness, finding that shape averageness, dimorphism, and skin texture symmetry are useful features capable of relatively accurate predictions, while shape symmetry is uninformative. However, the factors do not explain as much variance in attractiveness as the literature suggests. The implications for future research on attractiveness are discussed.

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