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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

Alex Jones Orcid Logo, Victor Shiramizu, Benedict C. Jones

British Journal of Psychology

Swansea University Author: Alex Jones Orcid Logo

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DOI (Published version): 10.1111/bjop.12717

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 trait...

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Published in: British Journal of Psychology
ISSN: 0007-1269 2044-8295
Published: Wiley 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa66755
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spelling v2 66755 2024-06-19 Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings a24e1e2a89b0a9120fe03b481a629edd 0000-0003-3600-3644 Alex Jones Alex Jones true false 2024-06-19 PSYS 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. Journal Article British Journal of Psychology 0 Wiley 0007-1269 2044-8295 computational modelling, methodology, person perception, perception,social cognition 17 6 2024 2024-06-17 10.1111/bjop.12717 COLLEGE NANME Psychology School COLLEGE CODE PSYS Swansea University SU Library paid the OA fee (TA Institutional Deal) Swansea University 2024-07-23T13:17:21.1541662 2024-06-19T12:36:15.4758669 Faculty of Medicine, Health and Life Sciences School of Psychology Alex Jones 0000-0003-3600-3644 1 Victor Shiramizu 2 Benedict C. Jones 3 66755__30678__f939ea67689b42b5aa9e268ad2ba53d0.pdf British J of Psychology - 2024 - Jones - Decoding the language of first impressions Comparing models of first impressions.pdf 2024-06-19T12:38:18.7021764 Output 975253 application/pdf Version of Record true © 2024 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
spellingShingle Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
Alex Jones
title_short Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
title_full Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
title_fullStr Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
title_full_unstemmed Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
title_sort Decoding the language of first impressions: Comparing models of first impressions of faces derived from free‐text descriptions and trait ratings
author_id_str_mv a24e1e2a89b0a9120fe03b481a629edd
author_id_fullname_str_mv a24e1e2a89b0a9120fe03b481a629edd_***_Alex Jones
author Alex Jones
author2 Alex Jones
Victor Shiramizu
Benedict C. Jones
format Journal article
container_title British Journal of Psychology
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publishDate 2024
institution Swansea University
issn 0007-1269
2044-8295
doi_str_mv 10.1111/bjop.12717
publisher Wiley
college_str Faculty of Medicine, Health and Life Sciences
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hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str School of Psychology{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}School of Psychology
document_store_str 1
active_str 0
description 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.
published_date 2024-06-17T13:17:20Z
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score 11.017776

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