Music Preferences and Interpersonal Perception — Study Notes
Study 1: What Do People Talk About As They Become Acquainted?
Context and goal
Investigate naturally occurring conversation topics as young adults become acquainted, to see whether music preferences are a prominent source of information about personality.
Participants
UT undergraduates: N=60; 55\%\text{ women}; mean age ar{X}=18.4 years, SD=0.94.
Ethnicity (reported): 4 Asian (8.5\%), 3 Hispanic (6.4\%), 36 White (76.6\%), 4 other (8.5\%).
Procedure
Over 6 weeks, participants interacted with a randomly assigned partner of the opposite or same sex via an online bulletin-board system.
No topic instructions; participants told to talk about anything that would help them get to know one another.
Coding of conversations used LIWC (Linguistic Inquiry and Word Count).
Seven topics coded: ext{books}, ext{clothing}, ext{movies}, ext{music}, ext{television shows}, ext{football}, ext{sports other than football}.
To cross-check topics, researchers examined Internet dating profiles for common topics (music, movies, books).
Football used as an extra sports category due to UT football prominence.
LIWC coding details
For each week, computed the percentage of participants who mentioned at least one keyword in each category.
The keywords defining the categories were chosen by two expert judges; full list available from the first author.
Note: one minor data note indicates that some participants listed entire albums instead of individual songs; when this occurred, two representative songs were chosen.
Key results
Music was the most commonly discussed topic overall.
Week 1: 58\% talked about music; next most common: 41\% talked about movies and 41\% talked about football.
Between-group differences: comparing music vs movies and music vs football yielded significant effects: t(58) > 2.1, p<0.05, d\approx0.50.
Across the 6 weeks, discussion of all preference domains declined, but music remained among the most discussed topics; only once in the first 5 weeks was music not the top topic.
Interpretation and conclusion
In a purely unconstrained communication context, music preferences still surfaced as a dominant topic in initial getting-to-know-you conversations.
Implication: music preferences may serve as an informative signal about personality during early acquaintances.
Additional notes
The University context, the online setting, and the age cohort (young adults) may influence the salience of music in conversations.
The study sets the stage for examining whether music preferences convey systematic interpersonal information beyond generic conversation cues.
Study 2: What Interpersonal Information Do Music Preferences Convey?
Theoretical background
Prior work suggests: (a) people believe music preferences reveal personality; (b) people use music preferences to convey information about themselves; (c) music preferences and personality are linked.
Research question: Do a target’s music preferences convey clear, consistent, and interpretable messages about personality? How accurate are these messages, and which cues drive impressions?
Framework
Brunswik’s lens model used to separate cue-utilization from cue-validity:
Cue-utilization: link between an observable cue (e.g., heavy-metal preference) and an observer’s judgment (e.g., agreeableness).
Cue-validity: link between the cue and the target’s actual construct level (e.g., true agreeableness).
If both cues are aligned, observer judgments should be accurate (observer accuracy).
Participants and targets
Targets: N=74 UT undergraduates; 40.5\% women; mean age ar{X}=18.9, SD=2.3.
Ethnicity (reported): 7 Asian ( 9.5\% ), 5 Hispanic ( 6.8\% ), 49 White ( 66.2\% ), 13 other ( 17.6\% ).
Stimuli and materials
Targets completed:
Personality measures (Big Five Inventory, BFI; 44 items).
Top-10 favorite songs: for each song, title, artist, and genre recorded. Targets were given an extra week to refine selections; songs were compiled onto a CD in listing order.
Observers: N=8 judges (5 women; mean age ar{X}=20.4, SD=3.1); listened to each top-10 CD and rated targets on personality and values.
Observers had no contact with targets; ratings were made independently after listening to CDs in random orders.
Measures
Target measures (self-reports):
Personality: Big Five Inventory (BFI).
Values: Rokeach Values Survey (terminal and instrumental values; rank-ordered).
Observer measures:
Personality: BFI (via observer ratings).
Values: Ratings on 12 terminal values and 6 instrumental values, as a reduced set from the full RVS.
Affect and self-esteem: self-esteem (single item), Positive and Negative Affect Schedule (PANAS) with 9 adjectives (subset of 20).
Self-esteem: Robins, Hendin, & Trzesniewski (2001) single-item measure.
Music-cue coding
Genre: derived from targets’ top-10 lists; 19 genres coded.
Specific features: rated by 3 coders for each song on 25 music-attribute dimensions (e.g., tempo, energy, singing, etc.).
Reliability: mean Cronbach’s alpha across the 25 attributes = \alpha\approx0.68.
Some procedures notes: 25 attributes were developed in prior work (Rentfrow & Gosling, 2003); the 25 items are available from the first author.
Observers’ judgments and data analysis
Inter-observer consensus: measured with the intraclass correlation (ICC; Shrout & Fleiss, 1979), specifically ICC(2,1).
Reported consensus across seven targets on BFI traits, values, and affect: mean ICCs(2,1) ≈ 0.29 (personality), 0.16 (values), 0.10 (affect).
Accuracy: correlations between observers’ aggregated ratings and targets’ self-ratings:
BFI traits: mean r\approx0.24.
Values: mean r\approx0.15.
Affect: mean r\approx0.10.
Openness showed the strongest consensus; Imagination among the strongest in both consensus and accuracy; Extraversion and other traits varied in consensus.
Accuracy pattern: more accurate for Openness and Imagination; less accurate for Ambition, Negative Affect, Self-Respect.
Context compared to prior zero-acquaintance work: music-based impressions are notably different from impressions based on photographs or brief video stimuli (e.g., Kenny, 1994). Music cues provided more information about Agreeableness, Emotional Stability, and Openness, and less about Extraversion and Conscientiousness.
Cue-utilization findings (how observers used music to judge targets)
Observers’ ratings of Extraversion were related to several music cues:
Attributes: energy, enthusiasm, amount of singing.
Genres: country; hip-hop.
Cue-validity findings (how well cues matched actual target traits):
Extraversion cue-valid indicators included high-energy music, enthusiasm, and singing, and country and hip-hop genres.
Example correlations (aggregated across cues): energy ≈ 0.12, enthusiasm ≈ 0.18, singing ≈ 0.29 for Extraversion; country ≈ 0.32; hip-hop ≈ 0.13.
Alignment of cue-utilization and cue-validity patterns
A composite index (vector correlations) assessed how well the observer cue-utilization patterns matched the cue-validity patterns across cues.
The vector correlations were large for attributes that were judged accurately, suggesting these cues mediated accurate impressions.
Openness and Imagination showed stronger vector correlations than Ambition and Negative Affect, indicating that for these traits, cues were more consistently predictive of true personality.
Comparisons to zero-acquaintance stimuli
Music-based impressions yield different accuracy profiles than face-based or video-based zero-acquaintance stimuli.
Music cues preferentially conveyed information about Agreeableness, Emotional Stability, and Openness, with less information about Extraversion and Conscientiousness,
highlighting that music information is not simply a proxy for general social cues but carries distinct personality signals.
Figures and supplementary analyses
Figure 2: Observer accuracy for music preferences vs. zero-acquaintance stimuli (Kenny, 1994); correlations corrected for unreliability and presented in Fisher’s z metric.
Figure 2 indicates that music cues can produce higher accuracy for certain traits than traditional zero-acquaintance cues, depending on the trait.
Full correlation tables for cue-utilization and cue-validity are available from the first author (not reproduced in full here).
Summary interpretation
Music preferences can convey consistent and accurate messages about personality when real targets’ lists (top-10 songs) are used as stimuli.
Observers can utilize both concrete song features and genre stereotypes to form impressions, though cue-validity varies by trait.
The processes are partially mediated by the degree to which cues align with the target’s actual personality profile.
General Discussion and Implications
Overall findings
Study 1 demonstrates that music is a common topic in getting-to-know-you conversations among strangers.
Study 2 shows that music preferences convey stable, accurate information about personality, with certain cues (specific music attributes and genres) driving impressions.
Music-based impressions differ qualitatively from traditional zero-acquaintance stimuli, suggesting music provides unique information about personality.
Mechanisms linking music and personality
Three potential mechanisms (independent or interacting):
Aesthetic pleasantries: music chosen for how it sounds, from low-level auditory features to higher-level cognitive associations (e.g., religious lyrics aligning with beliefs).
Arousal regulation: music chosen to regulate arousal levels (e.g., calm music for easygoing individuals).
Identity claims: people use music to convey self- and other-directed identities (e.g., intellectuals choose complex music to project sophistication).
Observers appear to have intuitive understandings of these links, but the exact processing steps require further study.
Cultural and contextual factors
Lay theories about links between music and personality vary with observers’ social status, country of residence, cohort, and historical era.
For example, jazz might be construed as erudite by younger observers but conventional by those who grew up when jazz was more mainstream.
Limitations and generalizability
Population: focused on young adults; may not generalize to older adults, for whom the importance of music decreases with age (LeBlanc et al., 1996).
Alternative cues in older samples may be finances, family, politics, etc.
Real-world relevance: despite limitations, contemporary music-sharing technologies (e.g., iTunes) increase the availability of music preferences for impression formation across ages. See Voida et al. (2005) for practices surrounding iTunes and work contexts where colleagues’ impressions are influenced by music collections.
Practical implications
In real-world settings (dating sites, workplaces), music preferences provide a compact, information-rich signal about personality that can influence initial impressions and judgments.
The findings support the notion that people strategically manage and interpret musical taste as part of social signaling.
Future directions
Elucidate underlying mechanisms in more detail: how aesthetic, arousal, and identity processes interact in music-personality inferences.
Test across broader age ranges and diverse cultural contexts to examine generalizability and cultural specificity.
Investigate longitudinal stability: how stable are music-based impressions over time as people learn more about each other?
Explore practical applications in recruiting, teamwork, and online dating, with attention to ethical considerations around stereotyping and biases.
Key Formulas, Statistics, and Concepts (summary)
Sample sizes and descriptives
Study 1: N=60; age ar{X}=18.4, ext{ SD}=0.94;
sex balance: 55\% women.Study 2: N=74; age ar{X}=18.9, ext{ SD}=2.3;
sex balance: 40.5\% women.
Timeframe and topics
Study 1 duration: 6 weeks.
Music as topic in Week 1: 58\%; movies and football each 41\%.
Statistical significance: t(58) > 2.1, ext{ } p<0.05, ext{ } d\approx 0.50.
Inventories and measures
Big Five Inventory (BFI): 44 items for personality; observer ratings vs self-ratings used for accuracy.
Values Survey (Rokeach): terminal and instrumental values; reduced set for observers (12 terminal, 6 instrumental).
Self-esteem: single-item measure (Robins, Hendin, & Trzesniewski, 2001).
Affect: PANAS; 9 adjectives used (subset).
Reliability and agreement
Music attributes: Cronbach’s alpha across 25 attributes: \alpha\approx0.68.
Interobserver consensus (ICC(2,1)) across eight judges:
BFI traits: ICC(2,1)\approx0.29;
Values: ICC(2,1)\approx0.16;
Affect: ICC(2,1)\approx0.10.
Observer accuracy: correlations with targets’ self-ratings:
BFI traits: r\approx0.24;
Values: r\approx0.15;
Affect: r\approx0.10.
Cue utilization and cue validity (examples)
Extraversion cues:
Music attributes: energy r\approx0.42, enthusiasm r\approx0.48, singing r\approx0.38.
Genres: country r\approx0.23, hip-hop r\approx0.33.
Extraversion cue validity (targets’ self-ratings): energy r\approx0.12, enthusiasm r\approx0.18, singing r\approx0.29, country r\approx0.32, hip-hop r\approx0.13.
Vector correlations: measure convergence of cue-utilization and cue-validity patterns across cues; larger values indicate closer alignment.
Key theoretical claims
Music preferences carry information not readily available from other zero-acquaintance cues.
Multiple mechanisms likely contribute to why music relates to personality, including aesthetic taste, arousal regulation, and identity signaling.
Citations and context (examples mentioned)
Brunswik (1956) lens model as analytic framework.
Prior zero-acquaintance work (e.g., Kenny, 1994).
Foundational measures: BFI (John & Srivastava, 1999); Rokeach Values Survey (Rokeach, 1973);
PANAS (Watson, Clark, & Tellegen, 1988).Related work on cues in social perception: Gosling et al. (2002); Vazire & Gosling (2004); Voida et al. (2005).
Limitations for interpretation
Generalizability to age groups beyond young adults remains uncertain.
Music’s importance likely shifts with age; other topics might become more informative in older samples.
Real-world applicability may vary with culture and evolving technology in music sharing.
Conclusion (concise)
Music preferences provide a unique, consistent, and sometimes highly accurate source of information about personality in everyday social perception, supplementing and in some cases surpassing traditional zero-acquaintance cues.