An Analysis of Internal Representations for Two Artificial Neural Networks that Classify Musical Chords

An Analysis of Internal Representations for Two Artificial Neural Networks that Classify Musical Chords

Vanessa Yaremchuk (McGill University, Canada)
DOI: 10.4018/978-1-60566-902-1.ch026
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Abstract

Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks Cognitive informatics is a field of research that is primarily concerned with the information processing of intelligent agents; it can be characterised in terms of an evolving notion of information (Wang, 2007). When it originated six decades ago, conventional accounts of information were concerned about using probability theory and statistics to measure the amount of information carried by an external signal. This, in turn, developed into the notion of modern informatics which studied information as “properties or attributes of the natural world that can be generally abstracted, quantitatively represented, and mentally processed” (Wang, 2007, p. iii). The current incarnation of cognitive informatics recognised that both information theory and modern informatics defined information in terms of factors that were external to brains, and has replaced this with an emphasis on exploring information as an internal property. This emphasis on the internal processing of information raises fundamental questions about how such information can be represented. One approach to answering such questions — and for proposing new representational accounts — would be to train a brain-like system to perform an intelligent task, and then to analyse its internal structure to determine the types of representations that the system had developed to perform this intelligent behaviour. The logic behind this approach is that when artificial neural networks
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Cognitive informatics has been applied to a wide variety of domains, ranging from organisation of work in groups of individuals (Wang, 2007) to determining the capacity of human memory (Wang, Liu & Wang, 2003) to modelling neural function (Wang, Wang, Patel & Patel, 2006). The studies described in this chapter provide an example of this approach in a new domain, musical cognition. There is a growing interest in the cognitive science of musical cognition, ranging from neural accounts of musical processing (Jourdain, 1997; Peretz & Zatorre, 2003) through empirical accounts of the perceptual regularities of music (Deutsch, 1999; Krumhansl, 1990) to computational accounts of the formal properties of music (Assayag, Feichtinger, & Rodrigues, 2002; Lerdahl & Jackendoff, 1983). Because music is characterised by many formal and informal properties, there has been a rise in interest in using connectionist networks to study it (Griffith & Todd, 1999; Todd & Loy, 1991).

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