A computer model with a musical education helps to reveal the music that many people prefer.

The human brain favours songs that are neither too simple nor too complex.

To identify the most rewarding type of music, a team led by Benjamin Gold at McGill University in Montreal, Canada, built a computer model to analyse 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 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.