Representing melodic relationships using network science.

Music is a complex system consisting of many dimensions and hierarchically organized information-the organization of which, to date, we do not fully understand. Network science provides a powerful approach to representing such complex systems, from the social networks of people to modelling the underlying network structures of different cognitive mechanisms. In the present research, we explored whether network science methodology can be extended to model the melodic patterns underlying expert improvised music.

Expert musical improvisations contain sequencing biases seen in language production.

Language production involves action sequencing to produce fluent speech in real time, placing a computational burden on working memory that leads to sequencing biases in production. Here we examine whether these biases extend beyond language to constrain one of the most complex human behaviors: music improvisation. Using a large corpus of improvised solos from eminent jazz musicians, we test for a production bias observed in language termed -a tendency for more accessible sequences to occur at the beginning of a phrase, allowing incremental planning later in the same phrase.