Dynamics of Music Cognition (2014-2018)

Musiikin kognition dynamiikka

Vastuullinen tutkija / Principal investigator: akatemiaprofessori Petri Toiviainen

Rahoittaja / Funding: Suomen Akatemia / Academy of Finland (2014-2018)

The aim of the project is to deepen our understanding about the role of body and brain in music perception. This question is tackled by studying the relationships between three kinds of dynamics present in music processing: musical feature dynamics, corporeal dynamics, and neural dynamics. The project comprises three facets. Facet 1 investigates how musical features are embodied and how these embodiments are affected by listener characteristics. Facet 2 studies how musical features are processed in the brain and how this processing is affected by listener characteristics. Finally, Facet 3 investigates the connection between embodiments of music and neural processing of music.

The project is highly interdisciplinary, combining methods of musicology, experimental psychology, movement science, neuroscience, and computer science. It will utilize cutting-edge research methodologies such as computational music analysis, optical motion capture, brain imaging, and machine learning. A unique aspect of the project is the use of a naturalistic paradigm, which utilizes real musical stimuli combined with computational music analysis (Music Information Retrieval) instead of synthetic ones to study the embodiments and neural correlates of music processing.

The project comprises a wide network of leading domestic and international partners. It builds on the research carried out at the Finnish Centre of Excellence in Interdisciplinary Music Research and the Finland Distinguished Professor (FiDiPro) project Machine Learning for Future Music and Learning Technologies.

The project will quantify how kinematic characteristics of music-induced movement depend on musical content, and how these characteristics are affected by musical training; pinpoint both invariant and variant (due to training, personality) features of neural processing of real music in terms of localization and efficiency; determine correlates between neural processing (e.g., strength and extent) and embodied processing (e.g., synchronization and complexity) of music; produce computational models that predict dynamic features of music-induced movement and temporal evolution of brain responses for a given piece of music; and develop new behavioural, neural, and computational methods for the study of dynamics of music cognition with real music. The obtained knowledge has applications, among others, in music therapy.