Scientists measure the variability in musical pieces.

Music is widely known to evoke emotions, but how exactly do these emotions arise, and how does meaning emerge from music? Nearly 70 years ago, music philosopher Leonard Meyer proposed that both are the result of an interplay between expectation and surprise. Throughout evolution, it has been essential for humans to make new predictions based on past experiences.

This is how we can also form expectations and predictions about the progression of music based on what we have heard. According to Meyer, emotions and meaning in music arise from the interplay of expectations and their fulfillment or (temporary) non-fulfillment.

A team of scientists led by Theo Geisel at the MPI-DS and the University of Göttingen have asked themselves whether these philosophical concepts can be quantified empirically using modern methods of data science.

In a paper published recently in Nature Communications, they used time series analysis to infer the autocorrelation function of musical pitch sequences; it measures how similar a tone sequence is to previous sequences. This results in a kind of “memory” of the piece of music. If this memory decreases only slowly with the time difference, the time series is easier to anticipate; if it vanishes rapidly, the time series offers more variation and surprises.