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Trustworthiness judgments without the halo effect: A data-driven computational modeling approach.

DongWon Oh1, Nicole Wedel2, Brandon Labbree3

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Summary
This summary is machine-generated.

Facial trustworthiness cues can be separated from attractiveness. Researchers found that faces manipulated for trustworthiness were perceived as more approachable and positive, not more attractive.

Keywords:
attractivenessface perceptionhalo effectjudgmentssocial perceptiontrustworthiness

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Area of Science:

  • Psychology
  • Computer Vision
  • Social Neuroscience

Background:

  • Perceived trustworthiness in faces is often linked to attractiveness.
  • The specific visual cues differentiating trustworthiness from attractiveness remain unclear.

Purpose of the Study:

  • To identify visual cues for perceived trustworthiness independent of attractiveness.
  • To investigate the role of approachability and facial expression in trustworthiness judgments.

Main Methods:

  • Development of data-driven models to manipulate perceived trustworthiness in faces.
  • Experimental designs (subtraction and orthogonal models) to control for attractiveness.
  • Human judgments and machine learning algorithms to assess facial perceptions.

Main Results:

  • Faces manipulated for trustworthiness were perceived as more trustworthy, but not more attractive.
  • These manipulated faces were also rated as more approachable and having more positive expressions.
  • Machine learning algorithms corroborated the increased perception of approachability and positive affect.

Conclusions:

  • Visual cues for trustworthiness and attractiveness can be dissociated.
  • Apparent approachability and facial emotion are key drivers of trustworthiness judgments.
  • These factors may also influence general evaluations of facial valence.