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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
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.
Keywords: computational modelling, methodology, person perception, perception,social cognition
College: Faculty of Medicine, Health and Life Sciences
Funders: Swansea University

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