Cognitive Models of Music Perception
Department of Human Sciences, Kanagawa University, Japan
Most people associate music with emotional and aesthetic awareness. The emotional awareness of music is not independent from understanding of musical structure. In other words, music perception, at least in part, underlies the emotional awareness of music. In this talk, I will discuss cognitive models of music perception.
As with language, infants are born culture-free with respect to music perception although they may have some innate constraints. As the growing-up process proceeds, listeners’ music perception is largely governed by their musical experiences and learning, or in psychological terms the ‘musical schema’ that reflects implicit knowledge acquired through long-term and everyday exposure to music of their own culture. Thus, when proposing a cognitive model, it should reflect culture-general properties only at a structure level while allowing for flexibility in acquiring and changing internal representations (which correspond to a concept of musical schemas) as a function of music exposure. One important issue is how to represent individual differences of musical schemas in a coherent way while keeping the fixed structure model.
In this talk, after overviewing how listeners perceive a sequence of tones as ‘music’ in their mind, I will focus on a perceptual process of pitch structure or ‘tonality’ perception. Subsequently, I will introduce several behavioral and neuroimaging studies that investigated differences and similarities in tonality perception between listeners of different ages and different music cultures who were at various levels of musical training. Based on evidence of these studies, I will describe fundamental characteristics of cognitive models of tonality perception.
obtained Ph. D. degree in Cognitive Psychology from
Hokkaido University in 2005. Since April 2017, she is currently an
Associate professor of Department Human Sciences, at Kanagawa
University. Prof. Matsunaga' s research goal is to understand how mechanisms
underlying perception of music arise and change from infancy through
adulthood as an experience in one's culture and cognitive (or universal)
Computing Systems: From Psychology to Engineering
ALICE, Aston University, UK
There are a number of fundamental changes in the way
computing systems are being developed, deployed and used.
They are becoming increasingly large, heterogeneous,
uncertain, dynamic and decentralised. These complexities
lead to behaviours during run time that are difficult to
understand, predict, and control. One vision for how to rise
to this challenge is to endow computing systems with
increased self-awareness, in order to enable more advanced
autonomous adaptive behaviour. A desire for self-awareness
has arisen in a variety of areas of computer science and
engineering over the last two decades.
In this context, we have developed a conceptual framework
that provides researchers with a common language with which
to describe the self-awareness capabilities of their
systems. Our framework is based on a developing fundamental
understanding of what self-awareness concepts might mean for
the design and operation of computing systems, drawing on
self-awareness theories from psychology and other related
I will show how explicit consideration of these concepts may
be beneficial in the engineering of adaptive computing
systems, that are better able to manage trade-offs between
conflicting goals in a complex environment at run time,
while reducing the need for a priori domain modelling at
design or deployment time.
I will discuss how computational self-awareness may include
knowledge of internal state, history, social or physical
environment, goals, and further, even a system's own way of
representing and reasoning about these things.
Finally, I will describe some of our work in some example
application domains: distributed smart-camera networks,
volunteer cloud computing, and mobile robotics, where
self-awareness can increase runtime adaptivity and
robustness, and avoid the need for a priori information at
Dr. Peter Lewis
is a Senior Lecturer in Computer Science at Aston University, in Birmingham. With a background in computational intelligence, Peter has made significant contribution to the field of self-aware computing, including the foundational book Self-aware Computing Systems: An Engineering Approach, in 2016. More broadly, his research is often inspired by biological, social and psychological processes, and advances our understanding about how to build complex computing systems that learn and adapt on an ongoing basis. Through ongoing industrial research collaborations, his work has been applied in areas such as smart camera networks, interactive music devices, avionics, manufacturing, and cloud computing. He obtained his PhD at the University of Birmingham, and is a member of the Aston Lab for Intelligent Collectives Engineering (ALICE).