Understanding the mind is one of the great "holy grails" of twentieth-century research. Regardless of training, most people who come in contact with the field of AI are at least partially motivated by the glimmer of hope that they will get a better understanding of the mind. This quest, of course, is a rich and complex one. It is easy to get mired in minutiae along the way, be they the optimization of an algorithm, the details of a mental model, or the intricacies of a logical argument. Thagard's book attempts to call us back to the larger picture and to draw in new devotees-and, in general, he succeeds.
This book begins, "Cognitive science is the interdisciplinary study of mind and intelligence,.." (p. ix); so, we assembled a cross-disciplinary review team that included researchers from the fields of Al, cognitive science, neuroscience, and philosophy. This multidisciplinary approach seemed appropriate because this book attempts to be a bridge book, written for a wide audience covering these areas and more.
The book is divided into two major sections: (1) Approaches to Cognitive Science and (2) Challenges to Cognitive Science. The first section is a survey of major trends in the research. The second section delineates and analyzes open problems and research issues.
Approaches to Cognitive Science
This section is a broad review of the literature and theories put forth to date. Thagard summarizes results of research to date: "the central hypothesis of cognitive science: Thinking can best be understood in terms of representational structures in the mind and computational procedures that operate on those structures" (p. 10). He uses a shorthand notation for this approach: the computational-representational understanding of mind (CRUM). One wonders at this point just how pessimistic his view will be of research to date. Is CRUM just a nice, pronounceable acronym, or does he intend to include all the overtones associated with the homonymous crumb (that is, crummy, crumbly, crumbling)? Section 2 shows that it is somewhere in between.
The chapters in this section are "Representation and Computation," "Logic," "Rules," "Concepts," "Analogies," "Images," "Connections," and "Review and Evaluation." From an AI perspective, these chapters adequately cover the research. Representation and computation are generally recognized as the two major divisions or perspectives on research. Logic, rules, and concepts (meaning frames, semantic networks, and so on) are widely used subdivisions. It is arguable whether the analogies section is at the same level of generality as the previous chapters, but there is a sufficient quantity of work in this area to constitute a separate chapter. The images chapter discusses visual images and processing. This discussion is necessarily at a high level and from the expected cognitive science perspective. Thus, it would be different from what an image-processing researcher might expect. However, the discussion is appropriate given the goal of the book and the perspective of the intended (general) audience. The connections chapter introduces connectionist and parallel distributed processing research in a general way.
Reading this text as a teacher of cognitive psychology, you can spot one of the book's strengths. Our cognitive psychologist writes, "Previously, I had been hesitant about covering cognitive science at any length in my cognitive psychology class, much less offering an entire course in cognitive science. I had yet to see an overview that was truly accessible to novices in the field." Cognitive science textbooks tend to lose the reader in a morass of labyrinthine detail. Thagard's style is clear and readable but still conveys the complexity and breadth of the issues involved. The organization is appealing; it is arranged around the type of mental representation (for example, logic, rules, concepts, images) instead of disciplinary line. This organization helps to make the text more accessible to everyone, regardless of his/her particular area of interest. …