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. |
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| 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] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| 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] |
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| ISSN: | 00071269 |
| DOI: | 10.1111/bjop.12717 |
