Decoding the language of first impressions: Natural language processing uncovers a two-topic structure in written descriptions of faces
Year: 2023
Type: preprint
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 2,222 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 ~48% of the variance in the topics extracted from free-text descriptions. Our results suggest that first impressions generated from free text are underpinned by two dimensions reflecting approach and avoidance assessments, respectively. While this model showed qualitative and quantitative overlap with models derived from trait ratings, a substantial amount of variance in the language used to describe first impressions is unaccounted for by explicit ratings.
Institutions: Swansea University, University of Strathclyde
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4386092853