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Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings.

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Title: Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings.
Authors: Jones, Alex L. (AUTHOR), Shiramizu, Victor (AUTHOR), Jones, Benedict C. (AUTHOR)
Source: British Journal of Psychology. May2026, Vol. 117 Issue 2, p725-740. 16p.
Subjects: Seasonal affective disorder, African Americans, Conceptual models, Personality assessment, Hispanic Americans, Anger, Natural language processing, Emotions, Social perception, White people, Age distribution, Descriptive statistics, Psychology, Research bias, Happiness, Comparative studies, Judgment (Psychology), Sentiment analysis, Data analysis software, Face perception, Facial expression
Geographic Terms: United States
Abstract: First impressions formed from facial appearance predict important social outcomes. Existing models of these impressions indicate they are underpinned by dimensions of Valence and Dominance, and are typically derived by applying data reduction methods to explicit ratings of faces for a range of traits. However, this approach is potentially problematic because the trait ratings may not fully capture the dimensions on which people spontaneously assess faces. Here, we used natural language processing to extract 'topics' directly from participants' free‐text descriptions (i.e., their first impressions) of 2222 face images. Two topics emerged, reflecting first impressions related to positive emotional valence and warmth (Topic 1) and negative emotional valence and potential threat (Topic 2). Next, we investigated how these topics were related to Valence and Dominance components derived from explicit trait ratings. Collectively, these components explained only ~44% of the variance in the topics extracted from free‐text descriptions and suggested that first impressions are underpinned by correlated valence dimensions that subsume the content of existing trait‐rating‐based models. Natural language offers a promising new avenue for understanding social cognition, and future work can examine the predictive utility of natural language and traditional data‐driven models for impressions in varying social contexts. [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.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings.
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  Data: First impressions formed from facial appearance predict important social outcomes. Existing models of these impressions indicate they are underpinned by dimensions of Valence and Dominance, and are typically derived by applying data reduction methods to explicit ratings of faces for a range of traits. However, this approach is potentially problematic because the trait ratings may not fully capture the dimensions on which people spontaneously assess faces. Here, we used natural language processing to extract 'topics' directly from participants' free‐text descriptions (i.e., their first impressions) of 2222 face images. Two topics emerged, reflecting first impressions related to positive emotional valence and warmth (Topic 1) and negative emotional valence and potential threat (Topic 2). Next, we investigated how these topics were related to Valence and Dominance components derived from explicit trait ratings. Collectively, these components explained only ~44% of the variance in the topics extracted from free‐text descriptions and suggested that first impressions are underpinned by correlated valence dimensions that subsume the content of existing trait‐rating‐based models. Natural language offers a promising new avenue for understanding social cognition, and future work can examine the predictive utility of natural language and traditional data‐driven models for impressions in varying social contexts. [ABSTRACT FROM AUTHOR]
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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=192785877
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        Value: 10.1111/bjop.12717
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 16
        StartPage: 725
    Subjects:
      – SubjectFull: Seasonal affective disorder
        Type: general
      – SubjectFull: African Americans
        Type: general
      – SubjectFull: Conceptual models
        Type: general
      – SubjectFull: Personality assessment
        Type: general
      – SubjectFull: Hispanic Americans
        Type: general
      – SubjectFull: Anger
        Type: general
      – SubjectFull: Natural language processing
        Type: general
      – SubjectFull: Emotions
        Type: general
      – SubjectFull: Social perception
        Type: general
      – SubjectFull: White people
        Type: general
      – SubjectFull: Age distribution
        Type: general
      – SubjectFull: Descriptive statistics
        Type: general
      – SubjectFull: Psychology
        Type: general
      – SubjectFull: Research bias
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      – SubjectFull: Happiness
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      – SubjectFull: Comparative studies
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      – SubjectFull: Judgment (Psychology)
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      – SubjectFull: Sentiment analysis
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      – SubjectFull: Data analysis software
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      – SubjectFull: Face perception
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      – SubjectFull: Facial expression
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      – SubjectFull: United States
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    Titles:
      – TitleFull: Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings.
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              Text: May2026
              Type: published
              Y: 2026
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