From Molecule to Metaphor: A Neural Theory of Language (Bradford Books) | 
 enlarge | Author: Jerome A. Feldman
Publisher: The MIT Press
Category: Book
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Media: Paperback
Number Of Items: 1
Pages: 384
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Dimensions (in): 8.9 x 5.8 x 0.9
ISBN: 0262562359
Dewey Decimal Number: 153
EAN: 9780262562355
ASIN: 0262562359
Publication Date: March 31, 2008
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Product Description
In From Molecule to Metaphor, Jerome Feldman proposes a theory of language and thought that treats language not as an abstract symbol system but as a human biological ability that can be studied as a function of the brain, as vision and motor control are studied. This theory, he writes, is a "bridging theory" that works from extensive knowledge at two ends of a causal chain to explicate the links between. Although the cognitive sciences are revealing much about how our brains produce language and thought, we do not yet know exactly how words are understood or have any methodology for finding out. Feldman develops his theory in computer simulations--formal models that suggest ways that language and thought may be realized in the brain. Combining key findings and theories from biology, computer science, linguistics, and psychology, Feldman synthesizes a theory by exhibiting programs that demonstrate the required behavior while remaining consistent with the findings from all disciplines.
After presenting the essential results on language, learning, neural computation, the biology of neurons and neural circuits, and the mind/brain, Feldman introduces specific demonstrations and formal models of such topics as how children learn their first words, words for abstract and metaphorical concepts, understanding stories, and grammar (including "hot-button" issues surrounding the innateness of human grammar). With this accessible, comprehensive book Feldman offers readers who want to understand how our brains create thought and language a theory of language that is intuitively plausible and also consistent with existing scientific data at all levels.
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 cognitive linguistics June 4, 2008
1 out of 1 found this review helpful
This is a fascinating book which encourages independent thought and inquiry into what we make assumptions about. I bought this book expecting it to be more academic than it was (neurologically). However I was pleasantly surprised by how easy it was to read and would recommend it to anyone interested in language
 A Key Stone to reorient AI/Natural language understanding October 8, 2007
6 out of 6 found this review helpful
Feldman's book "From Molecule to Metaphor" presents a new theory to understand natural languages. The new theory grounds solidly on research findings in neurology, cognitive psychology, and cognitive linguistics; it argues convincingly that mental activities are active neural connections and that basic language learning and understanding are embodied and that abstract concepts can be acquired through embodied concepts and metaphor. The theory reorients the research approach in AI/natural language understanding, and further leads to a unified cognitive theory and new technologies in dealing with semantics.
Based on this new theory, Feldman introduces four cognitive models which have been successfully developed in his research group. Regier's work simulates learning of spatial prepositions in the absence of explicit negative evidence and demonstrates how basic language learning and understanding are embodied in the physical environment. Bailey's work simulates acquisition and use of action verbs and shows that acquisition of action verbs are embodied in the motor control of the human body. Narayanan's work presents a method of understanding features of abstract events by metaphorically projecting them into concrete embodied domains. Chang's work simulates how children learn basic grammar and demonstrates that grammar learning is also grounded on embodied domains. The four models serve as fulcrums of the palace of a unified cognitive theory.
Feldman's new theory of natural language has received more and more interest in industry. In Europe it has been successfully used as a guiding theory in several industrial applications in dealing with semantics with uncertainty and vagueness, and as a supporting theory for certain high technology for the universal understanding interface for natural language.
Feldman's book is a keystone in AI/natural language understanding, which is suitable both for pioneering AI researchers and for ambitious industrial practitioners.
Commented by Dr. Tiansi Dong, Cognitive Ergonomic Systems, Germany,
email: tdong@cognesys.de
Remarkable, but perhaps too ambitious August 17, 2007
9 out of 11 found this review helpful
This is a remarkable book, albeit possibly too ambitious. Feldman has little use for Chomsky's theory of language (but admires his analysis of language structures). By analogy, he shows the same kinds of arguments could be used to show the ability to dance is genetic, and embodied in its own dedicated brain structure, as to say language is. What Feldman is about, though, is not to engage in polemics, but to attempt to develop a theory of language which draws on current knowledge and perspectives from a variety of fields. In fact, he says tongue in cheek, "the human genome does seem to code for a tendency to engage in bitter (academic) wars that are senseless to an outsider".
The key point is that any theory of language should reflect what we know about neuro-science. He implicitly makes the case that mastering language, while a wonderful achievement, is not much more amazing than mastering visual interpretation. There is a progression: one learns to control one's body, then use many of the same "mirror" neurons to interpret visually movements by others, than use many of the same neurons to give meaning to language describing movement. Grammar is a link between language and meaning, and is first learned by "matching sentences to what the child already knows visually". More abstract uses of language build on schema's for movement and emotional experience by way of metaphor, and Feldman does a wonderful job in giving the reader a great feel for how metaphor works, and how metaphor builds on past mastery of other metaphors organized in "cultural frames", and the role of parameterization.
Feldman may be overly ambitious in trying to communicate too much of the technical underpinnings, so that the reader sometimes gets bogged down. He does a nice job in giving some basics of neuro-science such as Hebb's rule for how neural connections are developed and embody learning. He nicely shows how triangular nodes can be used to represent and retrieve facts, and the neural basis for the well known concept of priming, in which visual or verbal cues impacts subsequent interpretation of language, when the two follow closely in time. However, he is less successful with belief networks, and "PDP connectionism" (what is known in other fields as neural network algorithms). Feldman uses the approach of providing toy problems, i.e. very simplified examples, and sometimes this just is not sufficient to get a real feel for things.
How do we learn, use and construct language? May 7, 2007
4 out of 4 found this review helpful
Jerome Feldman wrote a nice book on the question of how language came to be. It is readable for outsiders (and has some little jokes that make you smile once in a while). By taking you step to step to the concept of a neural theory of language the book can be read chapter by chapter. For me, the theory it develops looks quite satisfactory. Noam Chomsky is one of the guys I would like to comment on this book, maybe sometime in the future he does. Buy this book if you want to know how we construct and communicate information to others and how we use context, experience and grammar to understand. This is a typical American book, providing readers outside the field with a comprehensible overview of recent research. Well done!
 Wordy, convoluted, unsatisfying September 17, 2006
26 out of 41 found this review helpful
I read this book immediately after reading Hawkins' _On Intelligence_. Whereas OI was written in very layman's language, FMTMANTOL was written in a very academic style. Its style seemed to contradict the content. On the one hand, it gave some very simplified and hand-waving view of molecules, but went over the same simple points again and again in too much detail. Specifically, the author seemed fascinated with metaphor. Obviously that's the title of the book, but it seemed rather than just explaining how metaphor works, he as obsessed with pointing out the metaphors he was using to try to describe neurons and such. I don't have the book in front of me at the moment so I can't give any specific examples. Then (on the other hand), the meat of the book covers some theories on schemas and other logical/symbolic processing the brain allegedly does. This too is done in excruciating and difficult-to-read detail. The same points are repeated over and over in numerous ways, like one of those online personality tests that asks you the same question with slightly different wording. It seems that if he's covering things as simply as he does in the molecular section, then he doesn't need to go into too much detail, and certainly doesn't need the formal tone he takes. And if he's covering the more advanced stuff, then he also doesn't need to go into so much repetition that it seems he's writing for an unsophisticated audience.
I actually described the experience of reading it as "The most excruciating displeasure I've ever had".
Anyway, I think the book could have been 1/2 to 1/3rd the length it was.
Also, especially after reading Hawkins' book, I was especially disappointed that it didn't discuss the role of anatomy and brain structure. Certainly in a book that alleges to explain "from molecule to metaphor", there should be some discussion about brain regions, anatomy, cortex, etc. There wasn't even any relevant mention of how strong a role feedback plays.
He talks about molecules leading to synaptic transmission, but doesn't even show a spike train or time-voltage plot, as would be expected in even the most rudimentary exposition on neural function. A Hodgkins-Huxley model would also have gone well here as one level in between purely molecular function and neural transmission. He also could have/should have covered something about how memories are formed, at a molecular level (given the title of the book), rather than at the neural networks level, where he treats memories simply as synaptic weightings.
He seems to motivate his writing with how wonderful it'll be when we have intelligent machines, but he doesn't discuss anything other than some cheesy software models of "schemas", which seem to be able to parse natural languages, and says nothing of what people normally consider "real" intelligence. I think he should have made mention of the vast differences in processing power, power consumption (watts), operations/joule, memory, and associative power of the brain vs. real computers. I'm not a linguist or cognitive researcher, but my sense is that although schemas might be a theory of how it all works, it's not really what's going on. My analogy is that Newton had a theory of physics, but then Relativity adds more detail and classical physics "falls out" of more modern physics. I think schemas are like this: once we have a better (and artificially reproducible) theory of intelligence and language, then schemas as they are represented in this book will "fall out" of the theory.
Also, I'm puzzled about his insistence on equating intelligence with language. Although he is very clear about embodiment being a requirement for language, he makes little mention of how to get an artificial system to interact with the environment. Personally, I don't think you can begin to tackle language until you have a robot that can physically deal with the real world 1/10th as well as a real animal does -- something that can run on uneven terrain, use stereoscopic vision, and fumble for its keys in the dark. THAT to me is true embodiment, and until we have that figured out, I think theories about how language works with so-called schemas are too far out there to believe what's "really" going on in the brain. The schemas viewpoint of things seems waaay too much like computer science and trying to hack some way to parse a language without a lot of input.
The other stuff he should have mentioned, which, being an EE like me I think he should have, and being interested in artificial embodiment, is the research happening in neuromorphic engineering. People are using silicon to build neurally plausible *actual* systems, that work in hardware, without resorting to schemas, etc. They are mimicking cortex, visual areas, auditory function, etc., in real circuitry with similar orders of power, speed (faster than, even), and parallelism as real wetware. The advantage is of course that these circuits don't just simulate function like a digital computer, they *do* the function; he even says in his philosophical chapter at the end that the simulation of water, no matter how realistic, still isn't wet. Anyway, I'm getting off course here. The point is I think he gets way too far away from the physicality of the brain in trying to support his schemas idea.
Also, I don't think he did any of the research in his book himself. Not that that's such a bad thing, but given the level of wordiness, redundancy, and trying-to-explain-ness of it all, it almost seemed as though he were explaining his own theories. The book is a summary of other people's theories on language and not a whole lot on other things related to intelligence. One notably missing item in his explanation of language is the ability of humans to *hear* language. No serious mention of auditory processing or phonemic understanding is made. All the experiments on the computer are made with text.
Anyway, despite the shortcomings, some of the ideas are a little interesting. After all, I did finish the book. I think his ideas for language processing would be well suited for expert systems or other domains of limited scope. It was interseting to read about how non-english languages allow for variations in color understanding and word endings.
Overall I'm giving this book a crappy grade for style, readability, and comprehensibility. The subject matter really wasn't that difficult, but his writing styile made it so. The only reason it gets a 2 is because there was some interesting content. I thought it would extend my knowledge after reading Hawkins' book, but instead just made me more scornful of computer science oriented people trying to recreate "intelligence".
Also, some of my biases coming into this were having read Pinker's Blank Slate, Language Instinct, and How the Mind Works, and of course Hawkins' books. Also, I have done some graduate level research in neuromorphic engineering, which further biases my opinions towards that of feeling the need to embody physical stuff before you can think about tackling the language problem.
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