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Generative neural networks for experimental manipulation: Examining dominance‐trustworthiness face impressions with data‐efficient models.

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Title: Generative neural networks for experimental manipulation: Examining dominance‐trustworthiness face impressions with data‐efficient models.
Authors: Sobieszek, Adam (AUTHOR), Siemiątkowski, Maciej (AUTHOR), Imbir, Kamil K. (AUTHOR)
Source: British Journal of Psychology. May2026, Vol. 117 Issue 2, p636-655. 20p.
Subjects: Generative artificial intelligence, Prompts (Psychology), Database management, T-test (Statistics), Photography, Social perception, Structural equation modeling, Descriptive statistics, Experimental design, Crossover trials, Artificial neural networks, Personality, Judgment (Psychology), Comparative studies, Data analysis software, Facial expression, Face perception, Regression analysis
Abstract: An important development in the study of face impressions was the introduction of dominance and trustworthiness as the primary and potentially orthogonal traits judged from faces. We test competing predictions of recent accounts that address evidence against the independence of these judgements. To this end we develop a version of recent 'deep models of face impressions' better suited for data‐efficient experimental manipulation. In Study 1 (N = 128) we build impression models using 15 times less ratings per dimension than previously assumed necessary. In Study 2 (N = 234) we show how our method can precisely manipulate dominance and trustworthiness impressions of face photographs and observe how the effects' pattern of the cues of one trait on impressions of the other differs from previous accounts. We propose an altered account that stresses how a successful execution of the two judgements' functional roles requires impressions of trustworthiness and dominance to be based on cues of both traits. Finally we show our manipulation resulted in larger effect sizes using a broader array of features than previous methods. Our approach lets researchers manipulate face stimuli for various face perception studies and investigate new dimensions with minimal data collection. [ABSTRACT FROM AUTHOR]
Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Generative neural networks for experimental manipulation: Examining dominance‐trustworthiness face impressions with data‐efficient models.
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– Name: Abstract
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  Data: An important development in the study of face impressions was the introduction of dominance and trustworthiness as the primary and potentially orthogonal traits judged from faces. We test competing predictions of recent accounts that address evidence against the independence of these judgements. To this end we develop a version of recent 'deep models of face impressions' better suited for data‐efficient experimental manipulation. In Study 1 (N = 128) we build impression models using 15 times less ratings per dimension than previously assumed necessary. In Study 2 (N = 234) we show how our method can precisely manipulate dominance and trustworthiness impressions of face photographs and observe how the effects' pattern of the cues of one trait on impressions of the other differs from previous accounts. We propose an altered account that stresses how a successful execution of the two judgements' functional roles requires impressions of trustworthiness and dominance to be based on cues of both traits. Finally we show our manipulation resulted in larger effect sizes using a broader array of features than previous methods. Our approach lets researchers manipulate face stimuli for various face perception studies and investigate new dimensions with minimal data collection. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of British Journal of Psychology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=192785881
RecordInfo BibRecord:
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    Identifiers:
      – Type: doi
        Value: 10.1111/bjop.12732
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 20
        StartPage: 636
    Subjects:
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Prompts (Psychology)
        Type: general
      – SubjectFull: Database management
        Type: general
      – SubjectFull: T-test (Statistics)
        Type: general
      – SubjectFull: Photography
        Type: general
      – SubjectFull: Social perception
        Type: general
      – SubjectFull: Structural equation modeling
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Experimental design
        Type: general
      – SubjectFull: Crossover trials
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Personality
        Type: general
      – SubjectFull: Judgment (Psychology)
        Type: general
      – SubjectFull: Comparative studies
        Type: general
      – SubjectFull: Data analysis software
        Type: general
      – SubjectFull: Facial expression
        Type: general
      – SubjectFull: Face perception
        Type: general
      – SubjectFull: Regression analysis
        Type: general
    Titles:
      – TitleFull: Generative neural networks for experimental manipulation: Examining dominance‐trustworthiness face impressions with data‐efficient models.
        Type: main
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            NameFull: Sobieszek, Adam
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            NameFull: Siemiątkowski, Maciej
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            NameFull: Imbir, Kamil K.
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            – D: 01
              M: 05
              Text: May2026
              Type: published
              Y: 2026
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              Value: 117
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            – TitleFull: British Journal of Psychology
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