To answer the question of what cognitive psychology is you must first understand its theoretical foundations—foundations which have often received very little attention in modern textbooks. Author Michael Dawson seeks to address this oversight by exploring the essential principles that have established and guided this unique field of psychological study. Beginning with the basics of information processing, Dawson explores what experimental psychologists infer about these processes, and considers what scientific explanations are required when we assume cognition is rule-governed symbol manipulation. From these foundations, psychologists can identify the architecture of cognition and better understand its role in debates about its true nature. What is Cognitive Psychology? asks questions that will engage both students and researchers, including: Do we need the computer metaphor? Must we assume thinking involves mental representations? Do machines—or people—or brains—actually think? What is the "cognitive" in "cognitive neuroscience" and where is the mind? By establishing cognitive psychology’s foundational assumptions in its early chapters, this book places the reader in a position to critically evaluate such questions.

Cognitive science arose in the 1950s when it became apparent that a
number of disciplines, including psychology, computer science,
linguistics, and philosophy, were fragmenting. Perhaps owing to the
field's immediate origins in cybernetics, as well as to the
foundational assumption that cognition is information processing,
cognitive science initially seemed more unified than psychology.
However, as a result of differing interpretations of the foundational
assumption and dramatically divergent views of the meaning of the term
"information processing," three separate schools emerged:
classical cognitive science, connectionist cognitive science, and
embodied cognitive science.
Examples, cases, and research findings taken from the wide range of
phenomena studied by cognitive scientists effectively explain and
explore the relationship among the three perspectives. Intended to
introduce both graduate and senior undergraduate students to the
foundations of cognitive science, "Mind, Body, World" addresses
a number of questions currently being asked by those practicing in the
field: What are the core assumptions of the three different schools?
What are the relationships between these different sets of core
assumptions? Is there only one cognitive science, or are there many
different cognitive sciences? Giving the schools equal treatment and
displaying a broad and deep understanding of the field, Dawson
highlights the fundamental tensions and lines of fragmentation that
exist among the schools and provides a refreshing and unifying
framework for students of cognitive science.Michael R. W. Dawson is a professor of psychology at
the University of Alberta. He is the author of numerous scientific
papers as well as the books "Understanding Cognitive Science"
(1998), "Minds and Machines" (2004), "Connectionism: A
Hands-on Approach" (2005), and "From Bricks to Brains: The
Embodied Cognitive Science of LEGO Robots" (2010).

Previously, artificial neural networks have been used to capture only the informal properties of music. However, cognitive scientist Michael Dawson found that by training artificial neural networks to make basic judgments concerning tonal music, such as identifying the tonic of a scale or the quality of a musical chord, the networks revealed formal musical properties that differ dramatically from those typically presented in music theory. For example, where Western music theory identifies twelve distinct notes or pitch-classes, trained artificial neural networks treat notes as if they belong to only three of four different pitch-classes, a wildly different interpretation of the components of tonal music. Intended to introduce readers to the use of artificial neural networks in the study of music, this volume contains numerous case studies and research findings that address problems related to identifying scales, keys, classifying musical chords, and learning jazz chord progressions. A detailed analysis of networks is provided for each case study which together demonstrate that focusing on the internal structure of trained networks could yield important contributions to the field of music cognition.