The Cognitive Neuroscience of Music

Rating:
Summary: Very interesting overview of current research in the subject
Review: The collection of articles in this book gives a fascinating overview of human musical cognition and how it is modeled computationally. It also addresses the effect of brain lesions or abnormalities on musical competence and abilities, and thus gives the reader a taste of the kind of research that is being done in current research circles in the cognitive neuroscience of music. It is readily apparent after reading the articles that much is known about musical cognition, but there are many questions yet to answer. Because of space constraints, only a few of the articles will be reviewed here.

When considering human musical ability and competence it is natural to ask whether it is the result of evolutionary adaptations or whether it is "accidental" or "evolutionary vestige." The article by David Huron discusses these questions in some detail, with emphasis on the ability of evolution to shape not only physiological attributes and functions, but also human attitudes, emotions, cognitive abilities, and so on. The author gives an overview of the `nonadaptive pleasure seeking' (NAPS) view of music, and also the view that music is indeed an evolutionary vestige. He concludes, interestingly, that the truth of NAPS would place music lovers at an evolutionary disadvantage. If music is an evolutionary vestige, it still is important to ask, says the author, what value it had in the past for human survival. He discusses various types of evidence for supporting an evolutionary origin for music, such as genetic, neurological, ethological, and archaeological. Noting that no genes have been discovered which are correlated with musical ability, the other types of evidence do add plausibility to his evolutionary hypothesis, he argues at length in the article. The ability of music to form social bonds he believes shows the greatest promise as a plausible evolutionary origin for music. Most interesting is his discussion of how music brings about social bonding, with the hormone oxytocin playing a major role in this regard.

The article by Stephen McAdams and Daniel Matzkin on the perception of musical similarity is interesting for its own sake but also from the standpoint of artificial intelligence. Measures of similarity and to what extent a given concept can be changed and still be judged or perceived to be in the same category are of great interest in artificial intelligence. The authors of this article argue that the empirical evidence in similarity perception limits the `transformation space' for given music material. In other words, one can only go so far in the transformation of the original musical material before it is judged as completely new. The authors discuss in detail the factors that contribute to these limitations. In this context, the authors discuss a very interesting experiment to test among other things whether professional musicians are able to hear similarity to a greater degree of transformation if the transformations respect certain syntactical rules. The authors conclude, and their conclusions adhere to what is expected based on listening experiences, that the space of possible variations of musical material that is perpetually similar to an original piece of music is very limited.

Neural networks naturally enter into any discussion on human cognition, and they do so here in the article by Barbara Tillman, Jamshed Bharucha, and Emmanuel Bigand, who use them to model music cognition. Simulation of mental processes is of immense importance in brain research and allows one to study the effect of various anatomical and physiological abnormalities on cognition. The authors mention these capabilities in their article, but their emphasis is on explaining how neural networks coupled with unsupervised learning, can be used to model music cognition. They also mention, but do not discuss in any detail, the use of self-organizing maps to simulate the neural plasticity that allows the capacity to extract regularities and to then become sensitive to musical structures and regularities.

The article by John Brust discusses the effect of neurological disorders on musical function. The author discusses `musicogenic seizures', which are triggered by the hearing of music. Interestingly, these seizures can be triggered in some people by merely listening to their own voice. In some individuals, sound can also produce the perception of colors. This is called `synesthesia' by the author, but he does not discuss it in any great length. Apparently synesthesia is poorly understood, but has been noted to happen very frequently in individuals using hallucinogenic drugs. Also discussed is `amusia" which is an acquired impairments of musical processing.

The next article by Isabelle Peretz continues the discussion on amusia, but the emphasis is on what it reveals about brain specialization for music. The author holds that music has neuroanatomical specialization, in that there is a collection of neural networks that are dedicated to the processing of music. The author discusses various patients who had accidents causing brain damage in certain areas of the brain but were still able to retain musical skill. This occurred even when the damage occurred in the part of the brain responsible for language abilities. Even more surprising is that the auditory recognition of music is supported by cognitive processes that are not used at all in speech recognition or in environmental sound recognition. The author also discusses musical savants and the phenomenon of tone deafness. In terms of neural networks, the author asserts that brain specialization for music involves the encoding of pitch along musical scales and the ability to impute a regular beat to incoming events. She believes though that further research is needed to show that neural networks that are domain specific for music are the result of evolutionary adaptation.