To analyze music preferences in human subjects, Gold and colleagues built a computer model to analyze songs quantitatively. The researchers fed the model a large musical repertoire, including Canadian and German folk songs and Bach compositions. This training allowed the model to measure a trait that the researchers call ‘complexity’, which includes qualities such as how surprising a song would sound to listeners accustomed to Western music.
The results showed that the human brain favors songs that are neither too simple nor too complex. Surprising twists and turns in a piece of music can influence its appeal to the brain. The researchers asked people to rate how much they liked various musical clips, including excerpts from Georges Bizet’s opera ‘Carmen’ and the Japanese traditional song ‘Sakura’. Participants preferred songs of medium complexity to simple and highly complex tunes. When participants were uncertain about how a song would unfold, they preferred fewer surprises. But if people thought they knew what would happen next in a song, they enjoyed being surprised.
The results support existing theories that in many types of art, intermediate complexity maximizes curiosity and enjoyment.
Gold BP, Pearce MT, Mas-Herrero E, Dagher A and Zatorre RJ: Predictability and uncertainty in the pleasure of music: a reward for learning? J. Neurosci. [Epub ahead of print, 21 October 2019; 0428-19; DOI: https://doi.org/10.1523/JNEUROSCI.0428-19.2019].