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Poster B151 in Poster Session B - Thursday, August 8, 2024, 1:30 – 3:30 pm, Johnson Ice Rink
Personality traits predict the valence but not semantic content of narrative interpretations
Tory Benson1, Clara Sava-Segal1, Emily Finn1 (); 1Dartmouth College
Real-world scenarios often contain ambiguities that lead to differences in interpretation across individuals. These idiosyncrasies in be influenced by one’s personality traits. Here, participants viewed ambiguous, naturalistic images and were asked to generate their own interpretation of each. Trait data (positive affect and rumination) were also collected. We used state-of-the-art natural language processing (NLP) models to quantify interpretations’ valence and semantic content and examine their relationship to traits. Traits significantly influenced the valence of people's interpretations: higher positive affect predicted more positive valence, while higher rumination predicted more negative valence. In cross-validated analyses, these traits reliably predicted an unseen participant’s interpretation valence for a given image above chance. However, interpretations from individuals with similar trait scores were idiosyncratic in their semantic content, indicating that traits predispose valence, but not specific subject matter. Previous studies in this space have been primarily qualitative. Our project underscores how advancements in NLP tools have enabled a more objective, quantitative way to evaluate the role of traits in these interpretive processes.
Keywords: personality social perception natural language processing naturalistic stimuli