Name:Yanan Liu

School/Affiliation:McGill University

Co-Authors:Paul Masset, Robert Zatorre

Virtual or In-person:In-person

Short Bio:

Yanan is a Ph.D. student in Neuroscience at McGill University. Her research explores learning, intrinsic reward and the dopamine system in the human brain through the lens of music, using behavioral tasks, computational modeling, and neuroimaging techniques.
Prior to joining McGill, Yanan completed her master’s degree in Computer Science and Application at Psychology department, Beijing Normal University. Her previous work focused on the parcellation of the human brain's ventral tegmental area using diffusion MRI, as well as investigating the function and connectivity of its subregions using fMRI.

Abstract:

People often engage in intrinsically rewarding activities such as listening to music and solving puzzles. Yet, what makes these activities rewarding remains elusive. Music is not only a compelling example of intrinsic reward elicitor, but also an ideal medium for studying it. Prior studies have shown that when listening to music, the brain continuously anticipates the upcoming sound and learns the underlying structure, with higher prediction accuracy being associated with greater pleasure within certain range of predictability. However, these studies rely on computational estimates rather than experimental manipulation.
Here, we operationalize predictability through behavioral experiment, combine neuroimaging approaches and computational modeling to investigate the neurocomputational mechanisms of intrinsic reward. Specifically, we generate melodies using defined statistical pattern, measure participants’ trial-by-trial prediction accuracy, and test how both prediction performance and its improvement, i.e. learning progress, shape affective experience as sources of music reward. This work presents behavioral and computational modeling results that establish a basis for subsequent neuroimaging investigations to characterize the neural correlates of these rewarding sources in the dopaminergic system.
By clarifying how learning performance generates intrinsic reward in music, this study will provide fundamental insights into the neural mechanisms of internally motivated activities and the nature of broader aesthetic experiences. Beyond this, our findings may also inform educational approaches and inspire the design of intrinsically motivated artificial intelligence systems.