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Cognitive Science: An Introduction to the Science of the Mind Paperback – August 12, 2010

by José Luis Bermúdez (Author)
4.3 out of 5 stars 7 customer reviews
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Product Details

  • Paperback: 516 pages
  • Publisher: Cambridge University Press (August 12, 2010)
  • Language: English
  • ISBN-10: 0521708370
  • ISBN-13: 978-0521708371
  • Product Dimensions: 7.4 x 1 x 9.7 inches
  • Shipping Weight: 2.4 pounds (View shipping rates and policies)
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (7 customer reviews)
  • Amazon Best Sellers Rank: #797,269 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

3.0 out of 5 starsA new book on old-fashioned cognitive science
By whiteelephant on August 10, 2011
Format: Paperback
Cognitive Science is supposed to be an interdisciplinary field, drawing from developments in psychology, neuroscience, linguistics, and computer science. Unfortunately, somewhere along the way, the people calling themselves cognitive scientists began to distance themselves from developments in these fields, seemingly only listening in for tidbits that they wanted to hear (e.g. Marr's three levels of analysis). To be sure, 'cognitive science' was still developing, just increasingly under the names 'cognitive neuroscience', 'systems neuroscience', and 'cognitive psychology'. Just as early cognitive science took inspiration from the nascent field of artificial intelligence, today's fields take inspiration from modern work in machine learning, a far more powerful and successful descendent of good-old fashioned AI.

The 'cognitive scientists' just haven't kept up. That is evident in this new book by Bermudez (2010). Frankly, with the exception of some basic coverage of dynamical systems in the final chapter (entitled 'Looking Ahead'), the book feels like it could have been written twenty years ago. The debate about symbolic processing vs. connectionism is straight out of 80s, and fails to take into consideration the major developments in machine learning architectures since then, especially the shift from training networks via backpropagation to deep generative architectures (e.g. the work of Hinton and colleagues). Similarly, while there is much discussion about Marr's view of vision (1982), it isn't mentioned that this view was more or less rejected by neuroscientists in the 1990s, and our current view of vision is founded on generative models and Bayesian inference.Read more ›
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4.0 out of 5 starsA coherent and readable introduction to what cognitive science is
By A. Protopapas on December 18, 2011
Format: Paperback
Here is the conclusion of my review of this book for the journal MetaScience:

Overall, this is a superb introductory textbook. It would be possible, but hardly constructive, for anyone to criticize the selection of topics that were presented or left out. Bermúdez has cherry-picked theoretical topics, experimental findings, and methodological approaches, to put together a coherent and all-encompassing view of a unified science of the mind for the newcomer. In my opinion, there is nothing really crucial that isn't adequately presented. The prose is admirably fluid, the content is clear, and the stated intentions of the author seem well served. I am not entirely convinced that unsuspecting freshmen or sophomores will be able to get as much out of a course based on this book as it can offer, although this would depend largely on the general level of individual institutions. I am certain that this book can open up new worlds for juniors with previous exposure to empirical sciences and developed critical thinking skills, showing that the study of the mind lies along a continuum with the rest of the physical sciences, as a distinct level of analysis on top of neuroscience qua biology, rather than in some domain of unsubstantiated verbosity and conceptual confusion coming out of armchair theorizing, as certain approaches to psychology are sometimes perceived. In this respect, the book does inestimable service to the future of cognitive science by exposing future researchers to clear thinking in the application of multidisciplinary methods to intriguing questions about the structure and function of the mind.

The book is also well designed from a pedagogical point of view, providing many useful devices for study and integration.Read more ›
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4.0 out of 5 starsA must-read for cognitive scientists
By Kotoschov on April 13, 2012
Format: Paperback Verified Purchase
This is, by far, the best introduction to cognitive science available. Assuming no prior knowledge, this book encompasses all sub-areas of this field, and discusses many of the major problems related to overall cognition, this book is clear, awesome in its designed, accessible and what not. Fantastic.
It has, though, some unignoreable drawbacks. First, its organization is problematic. I am still unsure as to the coherence of the chapters and the ways they relate to each other. Additionally it is lacks many important scientific and computational details. Of course, it's just an introduction, but still I think it should have included more references to brain imaging studies, linguistic theory and algorithmic for cognitive modelling. Last thing, the references to cognitive models in the symbolic systems approach are much less common than references to any other kind of cognitive models. I do not agree with this, because I believe that cognitive models in the sybolic systems approach are the most theoretically crucial models in cognitive science.

Why? Non-computational models lack operative aspects; they pay almost no attention to questions of "How cognitive processes are executed? What are the algorithmic steps taken by the brain?". Computationl models which are not in the symbolic systems approach are problematic for yet abother reason. Their focus is statistical and very mathematical, and therefore they tend to be so precise that they provide no insight into the cognitive procedure. More concretely, if you have a connectionist model of some brain region, it provides you with knowledge of the brain region's electronic signals, but not with knowledge about the higher-level abstract actions commited by this region. As cognitive scientists, we are interested in the regularity that's behind cognitive processes, but connectionist models don't explain why some regularity R is as it is; they only create a system that produces R.
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