What do individual musical preferences say about personality? How does personality affect the type of music a person enjoys? And what does AI have to do with our musical preferences? The connection between music and psychology isn’t as much of a mystery as some might assume. Current research shows a strong connection between music and personality.
On episode 130 of The Science of Personality, cohosts Ryne Sherman, PhD, and Blake Loepp spoke with David Greenberg, PhD, research psychologist, social neuroscientist, and one of the world’s leading experts on the psychology of music. “The role of personality has a profound effect in terms of its link to musical preferences,” David said.
First, be aware this is an episode you’ll really want to listen to! David plays some music that informs part of the discussion. Now, keep reading to explore music and psychology, musical engagement, and the impact of AI.
Psychology and Musical Preferences
Our personality characteristics affect our musical preferences. Other factors, such as age and mental health, also influence why people are attracted to certain musical features.
Age and Developmental Stage
Musical preferences tend to fluctuate throughout childhood but become more stable around age 14. “The age of 14 is significant for neurobiological reasons in terms of language development and social identity,” David explained. During adolescence, preferences for intense or rebellious music are at their highest. Cultural environment and musical popularity trends also play a role in forming preferences during adolescence.
Throughout adulthood, large shifts in personality—and musical preferences—are much less likely because personality becomes more stable. Stability doesn’t exclude more subtle shifts in musical preferences at older developmental stages, however. According to David, preferences for mellow music increase somewhat in middle adulthood (along with the Big Five trait Agreeableness). And in later years, preferences for emotional and intellectual complexity tend to increase.
Personality Traits
David and other research psychologists run songs through a feature extraction process to extract up to 5,000 metrics and identify which features listeners are responding to, including mood and emotion. For instance, people associate major chords with happiness and minor chords with sadness. Minor chords don’t directly produce sadness, of course, but they do represent feeling sad.
In terms of the five-factor model, certain high or low traits relate to preferences for certain musical features. People who score high in Openness to Experience tend to prefer dissonant music, such as jazz. People who score low in Emotional Stability tend to prefer music that is brooding and angsty.
Preferences aligning with high or low Extraversion are just about what you’d expect, David said. People scoring high in Extraversion prefer music that is more upbeat, modern, danceable, and social. Introverted people prefer music that is more mellow, relaxing, contemplative, and suitable for solitary activities. “We see this not only from the listener standpoint but also from the performance standpoint. Extraverted musicians are going to perform music that would promote social engagements among the listeners,” he pointed out.
Mental Well-Being
Part of David’s research focuses on musical preferences in mental health and diagnostic assessments. He sees that people who score higher on measures of autistic traits prefer music with explicit patterns, repetition, and precise intonation. And those who have a diagnosis of depression prefer music with a range and complexity that reflects their day-to-day emotional experience.
David referenced Raymond Cattell as an inspiration for this research. A personality psychologist who studied music and psychology in the 1950s, Cattell posited that musical preferences provide a window into unconscious psychological traits and could prove to be effective for diagnosis.1, 2 Today at Chime Health AI, David uses big data and machine learning to verify Cattell’s hypothesis: “Over the course of several years, we collected data on musical preferences, personality, and mental health from more than 350,000 individuals. Our team has started to look at the extent to which a person’s combination of personality traits, demographics, and musical preferences can predict different mental health and neurodivergent conditions. We’re able to do that now with 80 to 85 percent accuracy.”
Musical Engagement and AI-Generated Music
Early in his research career, David developed a framework called the Engagement of Music Inventory (EMI), which measures five dimensions of musical engagement. For instance, is the listener dancing to the music? Are they experiencing emotional catharsis? Are they reflecting on the symbolism of the music? The inventory describes the main ways people engage with music:
- Analyzing – appreciating the technical aspects of music
- Healing – responding to music with emotion
- Dancing – responding to music with movement
- Storytelling – appreciating the symbolic and narrative aspects of music
- Bonding – feeling a sense of belonging and identity with others
“Musical preferences help us understand what people like, and musical engagement helps us understand how people are reacting to it,” David said. But with AI-generated music and recommendation algorithms now influencing our musical preference and engagement, the future of AI in psychology is poised to change.
In one future scenario, the human brain will not be able to distinguish between AI-generated music and music made by a human. In another, the brain will recognize those differences on some level, either consciously or subconsciously. “That’s going to help us understand the unique elements of human creativity,” David said. “That, to me, is fascinating.”
Another opportunity David sees is for AI to generate music that aligns with someone’s personality on demand. “We’re doing this too at Chime Health AI. Not only can we use musical preference as a screening to predict different conditions or traits, but we can also recommend music for and create music using AI that will be liked by those traits.”
Listen to this conversation in full on episode 130 of The Science of Personality. Never miss an episode by following us anywhere you get podcasts. Cheers, everybody!
References
- Cattell, R. B., & Anderson, J. C. (1953). The measurement of personality and behavior disorders by the I. P. A. T. Music Preference Test. Journal of Applied Psychology, 37(6), 446–454. https://doi.org/10.1037/h0056224
- Cattell, R. B., & Saunders, D. R. (1954). Musical Preferences and Personality Diagnosis: I. A Factorization of One Hundred and Twenty Themes. The Journal of Social Psychology, 39(1), 3–24. https://doi.org/10.1080/00224545.1954.9919099