Author: Oded Ben-Tal

Prediction learning is considered a ubiquitous feature of biological systems that underlies perception, action, and reward. For cultural artifacts such as music, isolating the genesis of reward from prediction is challenging, since predictions are acquired implicitly throughout life. Here, we examined the trajectory of listeners’ preferences for melodies in a novel musical system, where predictions were systematically manipulated. Across seven studies (n = 842 total) in two cultures, preferences scaled with predictions: participants preferred melodies that were presented more during exposure (global predictions) and that followed schematic expectations (local predictions). Learning trajectories depended on music reward sensitivity. Furthermore, fMRI showed that while auditory cortical activity reflects predictions, functional connectivity between auditory and reward areas encodes preference. The results are the first to highlight the hierarchical, relatively culturally-independent process by which predictions map onto reward. Collectively, our findings propose a novel mechanism by which the human brain links predictions with reward value.

Monday 27/6 at 4pm (UK)

From automated recommendation systems that suggest what music we should listen to next to the vast catalogues of musical metadata that underpin sound effect and other sonic databases, the explosion of the sound commodities into digital data has had a significant impact on how we search for, discover, and experience music and other audio content (i.e. podcasts, audiobooks, etc.). As streaming services like Spotify, Apple Music, Tencent and others become central hubs for the playback of a variety of sound commodities, these platforms create new opportunities and challenges for music-makers and their audiences. As “multi-sided markets”, these audio platforms bring into contact a number of (sometimes competing, sometimes complementing) interests, including but ot limited to musicians, advertisers, brands, labels, audiences, software designers, and data analysts. At the intersection of these interests lies a trove of data that helps coordinate how sound culture circulates.

Tuesday May 31st 3pm (UK)

(note different day and time)

The Music Engagement Research Initiative is an interdisciplinary research group at CCRMA, Stanford University. Through various approaches including EEG studies, mixed-methods user research, accessibility research, and analysis of industrial data, we seek to increase our understanding of how and why humans engage with music. I will speak about our group’s research and teaching efforts around data publication and re-use. Over recent years, we have come to treat data not only as a by-product of music science research, but also as intentional scientific contributions that can be used by others. I will discuss how such efforts by the scientific community can not only support reproducible research, but also facilitate new discoveries through improved access across research fields.

Monday March 28th 4pm (UK)

Renee Timmers’ current research projects investigate ensemble performance, in particular what visual and auditory nonverbal cues musicians use to coordinate and communicate with each other during performance.

Elaine Chew’s research centers on the mathematical and computational modeling of musical structures, with present focus on structures as they are communicated in performance and in ECG traces of cardiac arrhythmias.