Event details
Musicology Colloquium with Claire Pelofi
The Musicology Colloquium presents a talk by Claire Pelofi (neuroscience) from New York University.
Ticketing
Free, Unticketed
Bio
Claire Pelofi is a Research Scientist at the Center for Language, Music and Emotion, a research venture co-funded by NYU and the Max Planck Institute for Empirical Aesthetics in Frankfurt. She received a B.A. in Philosophy (2009) and a M.A in Cognitive Science (2012) at Ecole Normale Supérieure, Paris. Her Ph.D thesis focused on assessing the short and long-term effects of Auditory Scene Analysis in the context of music listening. She then joined Prof. Shihab Shamma’s lab in University of Maryland as a postdoctoral associate and worked in tight collaboration with Prof. Mary Farbood (Music and Audio Research Lab, NYU) on the cognitive constraints of music learning. Her research now spans various ways in which humans interact with music. She deploys a multifaceted approach that combines computational modeling of musical structures, machine learning, and the decoding of behavioral, biophysical and neural data. As a conservatory-trained violinist, Claire has performed in various ensembles in France and the US.
Abstract
Musical structures: Novel perspectives combining computation and cognition
In spite of its astonishing diversity across cultures, music is organized around foundational principles of temporal structures at different time-scales. These musical structures reflect properties of human cognitive systems. To gain insights into the interactions between musical structures and cognitive processes, I combine computational modeling of music stimuli with the decoding of behavioral and neural data. I also develop tools for computational musicology using machine learning techniques and human-machine interfaces to propel ecologically-valid research in music cognition. This interdisciplinary approach has contributed to novel perspectives on the stability of musical structures across cultures. Specifically, I will discuss how behavioral and neural data can be modeled to test hypotheses on the cognitive constraints that shape musical scales, and how computational frameworks can unfold the neuro-cognitive processes underlying musical learning and enjoyment.
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