  Music is an inherently social activity that allows people to share
experiences and feel connected with one another. There has been little progress
in designing artificial partners exhibiting a similar social experience as
playing with another person. Neural network architectures that implement
generative models, such as large language models, are suited for producing
musical scores. Playing music socially, however, involves more than playing a
score; it must complement the other musicians' ideas and keep time correctly.
We addressed the question of whether a convincing social experience is made
possible by a generative model trained to produce musical scores, not
necessarily optimized for synchronization and continuation. The network, a
variational autoencoder trained on a large corpus of digital scores, was
adapted for a timed call-and-response task with a human partner. Participants
played piano with a human or artificial partner-in various configurations-and
rated the performance quality and first-person experience of self-other
integration. Overall, the artificial partners held promise but were rated lower
than human partners. The artificial partner with simplest design and highest
similarity parameter was not rated differently from the human partners on some
measures, suggesting that interactive rather than generative sophistication is
important in enabling social AI.
