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Cognitive Science: An Introduction to the Science of the Mind. 3rd Edition
The Third Edition of this popular and engaging text consolidates the interdisciplinary streams of cognitive science to present a unified narrative of cognitive science as a discipline in its own right. It teaches students to apply the techniques and theories of the cognitive scientist's 'toolkit' - the vast range of methods and tools that cognitive scientists use to study the mind. Thematically organized, Cognitive Science underscores the problems and solutions of cognitive science rather than more narrowly examining individually the subjects that contribute to it - psychology, neuroscience, linguistics, and so on. The generous use of examples, illustrations, and applications demonstrates how theory is applied to unlock the mysteries of the human mind. Drawing upon cutting-edge research, the text has been substantially revised, with new material on Bayesian approaches to the mind and on deep learning. An extensive on-line set of resources is available to aid instructors and students alike. Sample syllabi show how the text can support a variety of courses, making it a highly flexible teaching and learning resource at both the undergraduate and graduate levels. Instructor and student resources available at
520 pages, Paperback
First published August 5, 2010
100 people are currently reading
929 people want to read
About the author
José Luis Bermúdez
24 books18 followersJosé Luis Bermúdez is Professor of Philosophy at Texas A&M University, where he previously served as Dean of the College of Liberal Arts and as Associate Provost for Strategic Planning. Before joining Texas A&M in 2010 he was Professor of Philosophy, Director of the Center for Programs in Arts and Sciences, and Director of the Philosophy-Neuroscience-Psychology Program at Washington University in St. Louis.
Dr. Bermúdez has more than 100 publications, including five single-author books and six edited volumes. His research interests are interdisciplinary in nature at the intersection of philosophy, psychology, and neuroscience. His first book, The Paradox of Self-Consciousness (MIT Press, 1998) analyzed the nature of self-awareness. Thinking without Words (Oxford UP, 2003) offered a model for thinking about the cognitive achievements and abilities of prelinguistic infants an nonlinguistiuc humans. Decision Theory and Rationality (Oxford UP, 2009) explores tensions in how the concept of rationality is defined and formalized in different academic disciplines. The second edition of his textbook Cognitive Science: An Introduction to the Science of the Mind was published by Cambridge University Press in March 2014. He is the editor of the New Problems in Philosophy book series, published by Routledge. Dr Bermudez is currently completing a book on the first person in language and thought, in addition to papers in the philosophy of mind and the theory of rationality.
Dr. Bermúdez has more than 100 publications, including five single-author books and six edited volumes. His research interests are interdisciplinary in nature at the intersection of philosophy, psychology, and neuroscience. His first book, The Paradox of Self-Consciousness (MIT Press, 1998) analyzed the nature of self-awareness. Thinking without Words (Oxford UP, 2003) offered a model for thinking about the cognitive achievements and abilities of prelinguistic infants an nonlinguistiuc humans. Decision Theory and Rationality (Oxford UP, 2009) explores tensions in how the concept of rationality is defined and formalized in different academic disciplines. The second edition of his textbook Cognitive Science: An Introduction to the Science of the Mind was published by Cambridge University Press in March 2014. He is the editor of the New Problems in Philosophy book series, published by Routledge. Dr Bermudez is currently completing a book on the first person in language and thought, in addition to papers in the philosophy of mind and the theory of rationality.
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Displaying 1 - 17 of 17 reviews
September 9, 2019
I've become increasingly fascinated by cognitive science, largely influenced by John Vervaeke. I wanted to pick up a textbook about it so I could get an introduction, and among the reputable ones, this was the shortest. When I first opened it I was a bit baffled. Crazy complex graphs that looked indecipherable. Thankful, I was mistaken. While difficult, most of them are quite accessible within the context of the book and the foundation it provides.
The first part starts with the pre-history of cognitive science. The reaction against behaviourism in psychology, due to some experiments showing learning without any reinforcement. Later studies showed that rats had "cognitive maps", which led to several studies in spatial learning that pointed out to minds having representations, in which the mind could no longer be ignored within the behaviourist paradigm. It laters explains the theory of computation and algorithms based on Turing machines, and how that laid the foundation to cognitive science, along with Chomsky's contribution to the structure of language. It finishes with how mental images are representations and the interdisciplinary model of vision, which is a good example of how cognitive science works (or ought to work).
The second part is all about the integration challenge. Cognitive science at its heart is an interdisciplinary endeavour, involving psychology, philosophy, linguistics, anthropology, neuroscience and artificial intelligence. But this creates the problem of lacking a unified theoretical framework that encompasses everything. It illustrates two ways of local integrations, the psychology of reasoning with evolutionary biology and game theory, and the connections between two different tools for studying brain activity (microelectrode recordings and functional neuroimaging).
The third part is about information-processing models of the mind. It starts with the physical symbolic system hypothesis, which claims a physical symbolic system is sufficient for general intention action (symbols being physical patterns). Then touches on the language of thought hypothesis from Jerry Fodor on how the physical symbolic system hypothesis deals with mental architecture - the syntax and semantics in a formal system. Next, it covers how it applies to the symbolic paradigm, touching on machine learning and several classic robots that ran on algorithms by manipulated physical symbol structures until a solution is found. One by using decisions trees, and the other with imagistic symbols. Then moves on to several types of neural networks (single-layer and multi-layer) and how they operate, including how they can be models of cognitive processes, like learning a language and object permanence in children.
The fourth part is about how the mind is organized. It covers in in-depth Fodor's modularity of the mind (that cognition is done by specific and independent modules), touching on its characteristics and how it frames cognitive science. Other hypotheses are covered, like the massive modularity hypothesis and hybrid architectures (where the mind uses both modular and non-modular processing). This is followed by strategies for brain mapping, an introduction to neuroscience and how it has helped cognitive science progress and confirm or disprove hypotheses.
The last part is about currently growing topics in cognitive science. It touches on dynamical systems - systems that evolve over time in a law dependent manner (like Newtonian mechanics), and how it can be used in cognitive science, illustrating it how it can be applied to child development in how they learn to walk and expectations of missing objects. Later it explored the situated cognition movement largely inspired by insects, given they have to solve very complex problems and yet they are a very basic organism. Neither dynamical systems or situated cognition fit in the typical information-processing paradigm of mainstream cognitive science.
Finally, it touches on consciousness. It starts from a philosophical stand-point about the hard problem of consciousness, and then illustrates some of the proposed approaches to tackling the problem (mostly by focusing on the so-called "easy" problems of consciousness instead). They're generally categorized as either studying the phenomenology of consciousness (what is it like to be conscious of something) and what one is consciousness or not (in a sort of Freudian manner).
Overall, I really enjoyed reading the book. I thought I was fairly familiar with the science of mind, but I was very wrong. While psychology gives a very solid foundation, cognitive science is another beast altogether. It's truly the future to understand ourselves and what made read the book. It's fairly dense and difficult, I can't say otherwise. Particularly when it ties to logic, mathematics or AI. But most topics I found accessible, and even if one does not understand all the details it nevertheless provides a lot of insight.
If you truly want to understand the mind, you need to learn about cognitive science, no way around it. This book provides a good introduction for it. It explains new concepts as they arise and contains helpful summaries at the end of each chapter.
This is likely not a super helpful review and maybe a bit confusing if one is not familiar with the topic, but it's very difficult to summarize given both the complexity and the amount of information it contains.
The first part starts with the pre-history of cognitive science. The reaction against behaviourism in psychology, due to some experiments showing learning without any reinforcement. Later studies showed that rats had "cognitive maps", which led to several studies in spatial learning that pointed out to minds having representations, in which the mind could no longer be ignored within the behaviourist paradigm. It laters explains the theory of computation and algorithms based on Turing machines, and how that laid the foundation to cognitive science, along with Chomsky's contribution to the structure of language. It finishes with how mental images are representations and the interdisciplinary model of vision, which is a good example of how cognitive science works (or ought to work).
The second part is all about the integration challenge. Cognitive science at its heart is an interdisciplinary endeavour, involving psychology, philosophy, linguistics, anthropology, neuroscience and artificial intelligence. But this creates the problem of lacking a unified theoretical framework that encompasses everything. It illustrates two ways of local integrations, the psychology of reasoning with evolutionary biology and game theory, and the connections between two different tools for studying brain activity (microelectrode recordings and functional neuroimaging).
The third part is about information-processing models of the mind. It starts with the physical symbolic system hypothesis, which claims a physical symbolic system is sufficient for general intention action (symbols being physical patterns). Then touches on the language of thought hypothesis from Jerry Fodor on how the physical symbolic system hypothesis deals with mental architecture - the syntax and semantics in a formal system. Next, it covers how it applies to the symbolic paradigm, touching on machine learning and several classic robots that ran on algorithms by manipulated physical symbol structures until a solution is found. One by using decisions trees, and the other with imagistic symbols. Then moves on to several types of neural networks (single-layer and multi-layer) and how they operate, including how they can be models of cognitive processes, like learning a language and object permanence in children.
The fourth part is about how the mind is organized. It covers in in-depth Fodor's modularity of the mind (that cognition is done by specific and independent modules), touching on its characteristics and how it frames cognitive science. Other hypotheses are covered, like the massive modularity hypothesis and hybrid architectures (where the mind uses both modular and non-modular processing). This is followed by strategies for brain mapping, an introduction to neuroscience and how it has helped cognitive science progress and confirm or disprove hypotheses.
The last part is about currently growing topics in cognitive science. It touches on dynamical systems - systems that evolve over time in a law dependent manner (like Newtonian mechanics), and how it can be used in cognitive science, illustrating it how it can be applied to child development in how they learn to walk and expectations of missing objects. Later it explored the situated cognition movement largely inspired by insects, given they have to solve very complex problems and yet they are a very basic organism. Neither dynamical systems or situated cognition fit in the typical information-processing paradigm of mainstream cognitive science.
Finally, it touches on consciousness. It starts from a philosophical stand-point about the hard problem of consciousness, and then illustrates some of the proposed approaches to tackling the problem (mostly by focusing on the so-called "easy" problems of consciousness instead). They're generally categorized as either studying the phenomenology of consciousness (what is it like to be conscious of something) and what one is consciousness or not (in a sort of Freudian manner).
Overall, I really enjoyed reading the book. I thought I was fairly familiar with the science of mind, but I was very wrong. While psychology gives a very solid foundation, cognitive science is another beast altogether. It's truly the future to understand ourselves and what made read the book. It's fairly dense and difficult, I can't say otherwise. Particularly when it ties to logic, mathematics or AI. But most topics I found accessible, and even if one does not understand all the details it nevertheless provides a lot of insight.
If you truly want to understand the mind, you need to learn about cognitive science, no way around it. This book provides a good introduction for it. It explains new concepts as they arise and contains helpful summaries at the end of each chapter.
This is likely not a super helpful review and maybe a bit confusing if one is not familiar with the topic, but it's very difficult to summarize given both the complexity and the amount of information it contains.
June 17, 2021
It’s one of the best of its kind. Maybe ‘Cognitive science based on the Information processing system ‘ would be a better topic for the essay. Author emphasis on the different approaches to the explanation of why cognitive abilities take form. The chapter about the ‘dynamical systems ‘ was extra. Indeed philosophical and social aspects of cognitive beings were neglected but that doesn't decrease the value of the book.
January 18, 2023
not a masterpiece because it struggles to establish its own footing in the more philosophical chapters and comes across as really underdeveloped there (the stuff on Searle's Chinese room for ex is a bit shallow and, frankly, kind of risible). Not cutting edge anywhere either, as to be expected. But a pretty handy introduction to "information processing" perspectives on mind=brain monism, great for beginners
September 19, 2025
Very clearly written and comprehensive intro to cognitive science. After each chapter the author cites other sources which can be jumping off points for deeper research of the topics. Highly recommend
June 15, 2025
Read only chapters: 1, 2, 3, 5, 6, 7, 8, 9, 10, 15, 16
January 3, 2017
Really frustrating experience. Do not recommend. There's a very accurate review/summary over on lesswrong (http://lesswrong.com/lw/il1/book_revi...).
I learned plenty from the book, but as So8res in the above review puts it, "the book was a lot of noise with very little signal". It could easily have been half the length. The target audience (if the author indeed intended one at all) is pitched far too low. His use and placement of diagrams indicates to me only that he knows textbooks like this should have diagrams - but he's not exactly sure what for.
Suggestion: take the contents at the begining of each chapter and just wiki it for clearer, shorter explanations.
I learned plenty from the book, but as So8res in the above review puts it, "the book was a lot of noise with very little signal". It could easily have been half the length. The target audience (if the author indeed intended one at all) is pitched far too low. His use and placement of diagrams indicates to me only that he knows textbooks like this should have diagrams - but he's not exactly sure what for.
Suggestion: take the contents at the begining of each chapter and just wiki it for clearer, shorter explanations.
November 3, 2018
I understand why some people didn't rate this book too highly, as it's rather intimidating if you don't have a background with AI or supplemental resources that you can pull from in order to better understand/relate to. It might help to read Haugeland, Turing, and Newell before studying this book.
I'd say this textbook is about average, I'm rating it with an additional star as I appreciated how interdisciplinary it is.
I'd say this textbook is about average, I'm rating it with an additional star as I appreciated how interdisciplinary it is.
unread
October 10, 2017http://lesswrong.com/lw/il1/book_revi... (anti-recommendation)
November 25, 2020
Very clear and up to date introduction to cognitive science.
October 20, 2021
This book provides a good introduction/review of some of the key ideas/Concepts of cognitive science such as:
C1) The mind as an information processsing system, cognition as info processing.
C2) The goal/challenge of integrating knowledge from psychology, philosophy of mind, computer science/artificial intelligence and other disciplines studying the mind in various ways.
C3) The 3 levels of analysis of classic cognitive science: computational, algorithmic, implementational.
C4) Mental architectures as organising frameworks for cognitive science.
C5) Symbolic vs neural networks/connectionist computation and hybrid approaches.
C6) Modularity of mind and key AI agent architectures.
There are other chapters covering consciouscness, folk psychology/theory of mind and neuroscience techniques.
The book provides reasonably good explanations with lots of details and examples of these concepts. Some quibbles/issues:
- terminologywise C1 is often labelled the Computational Theory of Mind (e.g see Stanford Encyclopedia of Philosophy). Bermudez adopts a narrower concept related to symbolic/linguistic thinking that is closer to a subtype of CTM.
- The difference between 'symbol' and 'representation' more generally could be better explained. I had to look at other sources to complete the book's explanations and to clarify the difference between symbolic and neurons based computation. The key feature of symbols is that they are discrete objects consisting of a finite number of subcomponents. The key feature of symbolic computation is that computations are specified directly over symbols/representations explicitly related to situations/the world. Connectionist models are distinguished by using implicit representations potentially distributed across many disparate neurons and their weights in various inputs to other neurons. Ironically, connectionist models are implemented using discrete symbolic computation on digitial computers but are meant to represent continuous representations.
- Discussion of mental architecture/organisation was incomplete. The book could have benefitted from dedicated chapters on memory, perception, motor control and decision making similar to e.g the Cambridge Handbook of cog sci (though discussions on these topics were spread in various points throughout the book).
- Unfortunately I read the 2014 2nd edition without realising that a 3rd edition had just come out. This is a pity because the previous editions are missing discussion of bayesian probabilistic models that are becoming central to extending symbolic cognitive architectures to allow for uncertainty and degrees of truth. The 3rd edition of the book apparently has a chapter on Bayesian cognitive science remedying this omission.
- Similarly, the book's discussion of neural net models is outdated, missing the evolution of deep learning since 2010. Again, I think this is remedied in the latest edition.
Overall, this is a good reference book on a fascinating topic. Its flaws are in large part an indication of the ongoing lack of consensus on a unifying mental architecture model of the mind/brain.
C1) The mind as an information processsing system, cognition as info processing.
C2) The goal/challenge of integrating knowledge from psychology, philosophy of mind, computer science/artificial intelligence and other disciplines studying the mind in various ways.
C3) The 3 levels of analysis of classic cognitive science: computational, algorithmic, implementational.
C4) Mental architectures as organising frameworks for cognitive science.
C5) Symbolic vs neural networks/connectionist computation and hybrid approaches.
C6) Modularity of mind and key AI agent architectures.
There are other chapters covering consciouscness, folk psychology/theory of mind and neuroscience techniques.
The book provides reasonably good explanations with lots of details and examples of these concepts. Some quibbles/issues:
- terminologywise C1 is often labelled the Computational Theory of Mind (e.g see Stanford Encyclopedia of Philosophy). Bermudez adopts a narrower concept related to symbolic/linguistic thinking that is closer to a subtype of CTM.
- The difference between 'symbol' and 'representation' more generally could be better explained. I had to look at other sources to complete the book's explanations and to clarify the difference between symbolic and neurons based computation. The key feature of symbols is that they are discrete objects consisting of a finite number of subcomponents. The key feature of symbolic computation is that computations are specified directly over symbols/representations explicitly related to situations/the world. Connectionist models are distinguished by using implicit representations potentially distributed across many disparate neurons and their weights in various inputs to other neurons. Ironically, connectionist models are implemented using discrete symbolic computation on digitial computers but are meant to represent continuous representations.
- Discussion of mental architecture/organisation was incomplete. The book could have benefitted from dedicated chapters on memory, perception, motor control and decision making similar to e.g the Cambridge Handbook of cog sci (though discussions on these topics were spread in various points throughout the book).
- Unfortunately I read the 2014 2nd edition without realising that a 3rd edition had just come out. This is a pity because the previous editions are missing discussion of bayesian probabilistic models that are becoming central to extending symbolic cognitive architectures to allow for uncertainty and degrees of truth. The 3rd edition of the book apparently has a chapter on Bayesian cognitive science remedying this omission.
- Similarly, the book's discussion of neural net models is outdated, missing the evolution of deep learning since 2010. Again, I think this is remedied in the latest edition.
Overall, this is a good reference book on a fascinating topic. Its flaws are in large part an indication of the ongoing lack of consensus on a unifying mental architecture model of the mind/brain.
November 30, 2017
If you want a book in which you will learn very little about Cognitive Science, but a whole lot of confusing details about AI, then this is the book for you. I hated reading this book, the writer assumes that you know everything about Cognitive Science and uses technical jargon throughout. Not for an introductory course, which is what I took.
May 31, 2019
The author is all kinds of full of himself.
This entire review has been hidden because of spoilers.
March 3, 2023
Very readable and nicely structured
July 4, 2013
Very interesting but very trivial.
October 13, 2016
Read only one chapter of the book, for Uni classes but it's pretty neat. Written in simple language, explains a lot about basics of Cognitive Science.
April 14, 2017
Excellent textbook on the sciences of the mind. Clear but needs very attentive reading. Not easy for neophytes
Displaying 1 - 17 of 17 reviews