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Understanding music and aging through the lens of Bayesian inference
Jiamin Gladys Heng1, Jiayi Zhang2, Leonardo Bonetti3
1School of Computer Science and Engineering, Nanyang Technological University, Singapore.
Neuroscience and Biobehavioral Reviews
|June 22, 2024
Summary
Bayesian inference explains how music prediction impacts emotion and learning. This framework also offers insights into aging, suggesting music may aid older adults by optimizing predictive processing.
Area of Science:
- Cognitive Neuroscience
- Psychoacoustics
- Gerontology
Background:
- Bayesian inference is increasingly used to model music perception and aging.
- Prediction is a core mechanism within Bayesian inference, explaining how musical elements influence behavior and emotion.
- Aging may involve changes in Bayesian inference, affecting uncertainty estimation and cognitive flexibility.
Purpose of the Study:
- To review the literature on predictive inferences in music perception and aging.
- To explore how Bayesian inference can unify concepts like musical expectancies, groove, and tension.
- To examine the potential of music interventions for older adults through a Bayesian lens.
Main Methods:
- Literature synthesis and theoretical review.
- Application of Bayesian inference principles to music perception.
- Analysis of aging as a process of Bayesian inference optimization.
Main Results:
- The Bayesian framework provides a unified account of music-related prediction, emotion, and learning.
- Aging may be characterized by increased reliance on prior knowledge and reduced updating of models.
- Music perception and engagement may be modulated by predictive processing, with implications for cognitive aging.
Conclusions:
- Bayesian inference offers a powerful framework for understanding music perception and its relationship to aging.
- Music interventions could leverage Bayesian principles to enhance cognitive function and well-being in older adults.
- Further research is needed to explore the therapeutic potential of music for aging populations using this framework.
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