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Area of Science:

  • Psychology
  • Cognitive Science
  • Computer Vision

Background:

  • Face impressions are crucial in social interactions, with dominance and trustworthiness being key judged traits.
  • Recent research questions the independence of these trait judgments, proposing competing theoretical accounts.
  • Existing methods for manipulating face impressions are often data-intensive and less experimentally flexible.

Purpose of the Study:

  • To develop a data-efficient deep model for manipulating face impressions.
  • To test competing predictions regarding the independence of dominance and trustworthiness judgments.
  • To propose an alternative account for the functional roles of these impressions.

Main Methods:

  • Developed a novel deep model for face impression generation, requiring significantly less data per dimension.
  • Conducted two studies (N=128, N=234) to build and validate the impression models.
  • Precisely manipulated dominance and trustworthiness impressions in face photographs.

Main Results:

  • Demonstrated the ability to build accurate impression models with 15 times less data than previously required.
  • Observed distinct patterns in how cues for one trait influence impressions of the other, challenging existing accounts.
  • Achieved larger effect sizes using a broader feature set compared to previous manipulation methods.

Conclusions:

  • Proposed an altered account where functional roles necessitate impressions of both dominance and trustworthiness to be based on cues of both traits.
  • The developed method offers a versatile tool for researchers to manipulate face stimuli efficiently.
  • Facilitates new investigations in face perception with reduced data collection requirements.