Saturday, September 13, 2008

The Psychology of Music Preferences

Apple has just released a version of iTunes with a new feature called Genius. Genius makes custom playlists (of either music you own or music you might want to by) based on a secret algorithm. If you opt-in, iTunes sends all of your listening history data (e.g., track names, artist names, playcounts, skipcounts) to a central server. The algorithm then looks for patterns in worldwide listening trends. To use Genius you right-click a particular song, choose 'start Genius', and BANG you've got a list of 'similar' songs. I'm loving it. It's helped me rediscover some music that Dan had given me but that I hadn't listened to much.

This got me thinking about how one might statistically look for trends in music preferences. I wondered if there'd ever been a factor analysis of music preferences. A factor analysis is a statical technique for finding trends amongst different variables. It's often used in personality research. You ask a large sample of volunteers a whole stack of questions (e.g., "On a scale of 1 to 10 how much do you like parties?", "How much do you like being the center of attention?" etc.) and look for shared variance in the responses. I've written about factor analyses of personality related data before here.

Anyway, a quick literature search turned up this article in the Journal of Personality and Social Psychology:

Rentfrow, P.J., Gosling, S.D. (2003). The Do Re Mi’s of Everyday Life: The Structure and Personality Correlates of Music Preferences. Journal of Personality and Social Psychology, 84(6), 136-1256.

Among the studies reported is a factor analytic study of music preferences. 1,704 students from the University of Texas were asked to rate each of 14 music genres on a scale from 1 ('I don't like it at all') to 7 ('I like it a great deal'). The genres were: alternative, blues, classical, country, electronica/dance, folk, heavy metal, rap/hip-hop, jazz, pop, religious, rock, soul/funk, and sound tracks.

The analysis revealed 4 main dimensions (factors) that captured 59% of the total variance. The names given to these factors and the genres associated with them are as follows:

- Reflective and complex (blues, jazz, classical, and folk)
- Intense and rebellious (rock, alternative, heavy metal)
- Upbeat and Conventional (country, sound track, religious, and pop)
- Energetic and Rhytmic (rap/hip-hop, soul/funk, and electronica/dance)

These dimensions are reasonably independent of each other (1). People who like reflective and complex music are just as likely to enjoy intense and rebellious music as they are to not. What these factors mean is that if someone likes a genre related to a particular dimension (e.g., blues) then they'll probably also like the other genres on that dimension (e.g., jazz). The same goes for disliking a genre.

One limitation of this study is that peoples' understanding of genre terms may vary. I might think that I don't like folk music and yet like many songs that others would categorise as folk. It would be great to see an analysis done on song by song ratings, rather than just genres.

Another analysis, which was really interesting, involved looking for relationships between e musical preferences and differences in personality and cognitive ability. They found all sorts of relationships, although most of them were quite small (.2ish). The largest one (.4ish) was between a preference for Reflective and Complex music and the personality characteristic Openness to Experience. Interestingly, there was a small (.2ish) relationship between verbal IQ and liking of Reflective and Complex, Intense and Rebellious, or Upbeat and Conventional music (2).

Very interesting stuff.

I'd love to see these researchers team up with Apple and analyse the iTunes Genius data.


(1) Upbeat and Conventional and Energetic and Rythmic correlate .5 if allowed to covary.

(2) And no, I don't think this is evidence that music makes you smarter (can you guess why?).

1 comment:

Anonymous said...

Record companies use musical analysis software to identify patterns in music in order to compare a song with past music hits. This comparison assists in decision making regarding signing on musical artist or to predict record sales.

Problem, I doubt that they would share this information as it provides them with market advantage. Although it would be interesting to know if each company has their own proprietary software or if they use a generic program that allows them some variable input.