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. 2022 Dec;48(12):1635-1650.
doi: 10.1177/01461672211048291. Epub 2021 Sep 28.

Appearance Reveals Music Preferences

Affiliations

Appearance Reveals Music Preferences

Laura Tian et al. Pers Soc Psychol Bull. 2022 Dec.

Abstract

Disclosing idiosyncratic preferences can help to broker new social interactions. For instance, strangers exchange music preferences to signal their identities, values, and preferences. Recognizing that people's physical appearances guide their decisions about social engagement, we examined whether cues to people's music preferences in their physical appearance and expressive poses help to guide social interaction. We found that perceivers could detect targets' music preferences from photos of their bodies, heads, faces, eyes, and mouths (but not hair) and that the targets' apparent traits (e.g., submissiveness, neatness) undergirded these judgments. Perceivers also desired to meet individuals who appeared to match their music preferences versus those who did not. Music preferences therefore seem to manifest in appearance, regulating interest in others and suggesting that one's identity redundantly emerges across different types of cues. People may thus infer others' music preferences to identify candidates for social bonding.

Keywords: accuracy; face perception; music; person perception; preferences.

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Conflict of interest statement

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Figures

Figure 1.
Figure 1.
Example stimuli across photo conditions. Note. This target reported enjoying reflective/complex music the most and enjoying upbeat/conventional music the least.
Figure 2.
Figure 2.
Standardized regression coefficients and confidence intervals for targets’ actual music preference regressed on overall perceived music dimension preference in Study 1. Note. Coefficients based on perceived music dimension preference ratings averaged across standardized and spontaneous poses, adjusted for target age, race, and gender. Error bars represent 95% confidence intervals.

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