[ExI] Fwd: [EP_group] Book: Mind as Machine: A History of Cognitive Science

[hkhenson]

Forwarded from an EP group

>  Mechanical Mind Gilbert Harman
>Mind as Machine: A History of Cognitive Science. Margaret A. Boden. Two
>volumes, xlviii + 1631 pp. Oxford University Press, 2006. $225.
>
>The term cognitive science, which gained currency in the last half of
>the 20th century, is used to refer to the study of
>cognition?cognitive structures and processes in the mind or brain,
>mostly in people rather than, say, rats or insects. Cognitive science in
>this sense has reflected a growing rejection of behaviorism in favor of
>the study of mind and "human information processing." The field includes
>the study of thinking, perception, emotion, creativity, language,
>consciousness and learning. Sometimes it has involved writing (or at
>least thinking about) computer programs that attempt to model mental
>processes or that provide tools such as spreadsheets, theorem provers,
>mathematical-equation solvers and engines for searching the Web. The
>programs might involve rules of inference or "productions," "mental
>models," connectionist "neural" networks or other sorts of parallel
>"constraint satisfaction" approaches. Cognitive science so understood
>includes cognitive neuroscience, artificial intelligence (AI), robotics
>and artificial life; conceptual, linguistic and moral development; and
>learning in humans, other animals and machines.
>
>[Human Information Processing ] In 1972, Peter Lindsay and
>Donald Norman published a textbook that made the computational approach
>to psychology a familiar part of the undergraduate experience. The book
>was illustrated with memorable diagrams showing the "data,"
>"computational," "cognitive" and "decision" demons of Oliver Selfridge's
>Pandemonium program at work. The demons were "mindlike software
>'agents,'" able to cooperate and communicate with each other and with a
>human user. From Mind as Machine.
><http://www.americanscientist.org/BookReviewTypeDetail/assetid/56418#564\
>56>
>
>Among those sometimes identifying themselves as cognitive scientists are
>philosophers, computer scientists, psychologists, linguists, engineers,
>biologists, medical researchers and mathematicians. Some individual
>contributors to the field have had expertise in several of these more
>traditional disciplines. An excellent example is the philosopher,
>psychologist and computer scientist Margaret Boden, who founded the
>School of Cognitive and Computing Sciences at the University of Sussex
>and is the author of a number of books, including Artificial
>Intelligence and Natural Man (1977) and The Creative Mind (1990). Boden
>has been active in cognitive science pretty much from the start and has
>known many of the other central participants.
>
>In her latest book, the lively and interesting Mind as Machine: A
>History of Cognitive Science, the relevant machine is usually a
>computer, and the cognitive science is usually concerned with the sort
>of cognition that can be exhibited by a computer. Boden does not discuss
>other aspects of the subject, broadly conceived, such as the "principles
>and parameters" approach in contemporary linguistics or the psychology
>of heuristics and biases. Furthermore, she also puts to one side such
>mainstream developments in computer science as data mining and
>statistical learning theory. In the preface she characterizes the book
>as an essay expressing her view of cognitive science as a whole, a
>"thumbnail sketch" meant to be "read entire" rather than "dipped into."
>
>It is fortunate that Mind as Machine is highly readable, particularly
>because it contains 1,452 pages of text, divided into two very large
>volumes. Because the references and indices (which fill an additional
>179 pages) are at the end of the second volume, readers will need to
>have it on hand as they make their way through the first. Given that
>together these tomes weigh more than 7 pounds, this is not light
>reading!
>
>Boden's goal, she says, is to show how cognitive scientists have tried
>to find computational or informational answers to frequently asked
>questions about the mind?"what it is, what it does, how it works,
>how it evolved, and how it's even possible." How do our brains generate
>consciousness? Are animals or newborn babies conscious? Can machines be
>conscious? If not, why not? How is free will possible, or creativity?
>How are the brain and mind different? What counts as a language?
>
>The first five chapters present the historical background of the field,
>delving into such topics as cybernetics and feedback, and discussing
>important figures such as René Descartes, Immanuel Kant, Charles
>Babbage, Alan Turing and John von Neumann, as well as Warren McCulloch
>and Walter Pitts, who in 1943 cowrote a paper on propositional calculus,
>Turing machines and neuronal synapses. Boden also goes into some detail
>about the situation in psychology and biology during the transition from
>behaviorism to cognitive science, which she characterizes as a
>revolution. The metaphor she employs is that of cognitive scientists
>entering the "house of Psychology," whose lodgers at the time included
>behaviorists, Freudians, Gestalt psychologists, Piagetians, ethologists
>and personality theorists.
>
>Chapter 6 introduces the founding personalities of cognitive science
>from the 1950s. George A. Miller, the first information-theoretic
>psychologist, wrote the widely cited paper "The Magical Number Seven,
>Plus or Minus Two," in which he reported that, as a channel for
>processing information, the human mind is limited to about seven items
>at any given time; more information than that can be taken in only if
>items are grouped as "chunks." Jerome Bruner introduced a "New Look" in
>perception, taking it to be proactive rather than reactive. In A Study
>of Thinking (1956), Bruner and coauthors Jacqueline Goodnow and George
>Austin looked at the strategies people use to learn new concepts.
>Richard Gregory argued that even systems of artificial vision would be
>subject to visual illusions. Herbert Simon and Allen Newell developed a
>computer program for proving logic theorems. And Noam Chomsky provided a
>(very) partial generative grammar of English in Syntactic Structures
>(1957).
>
>Two important meetings occurred in 1956, one lasting two months at
>Dartmouth and a shorter one at MIT. There was also a third meeting in
>1958 in London. Soon after that, Miller, Eugene Galanter and Karl
>Pribram published an influential book, Plans and the Structure of
>Behavior (1960), and Bruner and Miller started a Center for Cognitive
>Studies at Harvard. These events were followed by anthologies, textbooks
>and journals. "Cognitive science was truly on its way."
>
>In the remainder of Boden's treatment, individual chapters offer
>chronological accounts of particular aspects of the larger subject. So,
>chapter 7 offers an extensive discussion of computational psychology as
>it has evolved since 1960 in personality psychology, including emotion;
>in the psychology of language; in how psychologists conceive of
>psychological explanation; in the psychology of reasoning; in the
>psychology of vision; and in attitudes toward nativism. The chapter then
>ends with an overview of the field of computational psychology as a
>whole. Boden acknowledges that "we're still a very long way from a
>plausible understanding of the mind's architecture, never mind computer
>models of it," but she believes that the advent of models of artificial
>intelligence has been extraordinarily important for the development of
>psychology.
>
>Chapter 8 discusses the very minor role of anthropology as the
>"missing," or "unacknowledged," discipline of cognitive science. Here
>Boden touches on the work of the relatively few anthropologists who do
>fit into cognitive science.
>
>Chapter 9, the last in volume 1, describes Noam Chomsky's early impact
>on cognitive science, discussing his famous review of B. F. Skinner's
>book Verbal Behavior, his characterization of a hierarchy of formal
>grammars, his development of transformational generative grammar and his
>defense of nativism and universal grammar. Boden notes that
>psychologists, including Miller, lost interest in transformational
>grammar after realizing that the relevant transformations were ways of
>characterizing linguistic structure and not psychological operations.
>
>As Boden mentions, many people, including me, raised objections in the
>1960s to Chomsky's so-called nativism?his view that certain
>principles of language are innate to a language faculty. She seems
>unaware that Chomsky's reasons for this view became clearer as time went
>on and formed the basis for the current, standard
>principles-and-parameters view, which explains otherwise obscure
>patterns of differences between languages.
>
>Perhaps the heart of Boden's story is her account of the development of
>artificial intelligence, broadly construed. There were two sorts of
>artificial intelligence at the beginning: One treated beliefs and goals
>using explicit languagelike "propositional" representations, whereas the
>other?the connectionist approach?took beliefs and goals to be
>implicitly represented in the distribution of excitation or connection
>strengths in a neural network.
>
>The proposition-based approach, outlined in chapter 10, initially
>developed programs for proving theorems and playing board games. These
>were followed by studies of planning, puzzle problem solving, and expert
>systems designed to provide medical or other advice. Special programming
>languages were devised, including LISP, PROLOG, PLANNER and CONNIVER.
>Systems were developed for default reasoning: For instance, given that
>something is a bird, assume it flies (in the absence of some reason to
>think it does not fly); given that it is a penguin, assume it does not
>fly (in the absence of some reason to think it does fly).
>
>There were difficulties. One was "computational complexity"?almost
>all methods that worked in small "toy" domains did not work for more
>realistic cases, because of exponential explosions: Operating in even
>slightly more complex domains took much longer and used many more
>resources. Another issue was whether "frame" assumptions (such as that
>chess pieces remain in the same position until captured or moved) should
>be built into the architecture of the problem or should be stated
>explicitly. This became a pressing issue in thinking about general
>commonsense reasoning: Is it even possible to explicitly formulate all
>relevant frame assumptions?
>
>On the other side was the connectionist neural-net approach, considered
>in chapter 12, which seeks to model such psychological capacities as
>perception, memory, creativity, language and learning, using
>interconnected networks of simple units. Connectionism was
>­especially concerned with rapidly recognizing and classifying items
>given their observed characteristics, without having to go through a
>long, complicated chain of reasoning.
>
>In the simplest case of a single artificial perceptron, several
>real-number inputs represent the values of selected aspects of the
>observed scene, and an output value (the activation of the perceptron in
>question), possibly 1 or 0, indicates yes or no. The perceptron takes a
>weighted sum of the input values and outputs 1, or yes, if the sum is
>greater than some threshold value; if not, the output is 0. Perceptrons
>can be arranged in feed-forward networks, so that the output of the
>first layer goes to perceptrons in the second layer, whose outputs are
>inputs to a third layer, and so on until a decision is made by a final
>threshold unit. Given appropriate weights and enough units, a
>three-layer network can approximate almost any desired way of
>classifying inputs. Relevant weights do not need to be determined ahead
>of time by the programmer. Instead, the network can be "trained" to give
>desired outputs, by making small corrections when the network's response
>is incorrect.
>
>There are other kinds of connectionist networks. For example, in certain
>sorts of recurrent networks, the activations of the units settle into a
>more or less steady state.
>
>Boden describes these developments in loving detail, along with bitter
>disputes between proponents of proposition-based research and those who
>favored the connectionist approach. The disagreements were fueled by
>abrupt changes in U.S. government funding, which are noted in chapter
>11. Much of the government money available was provided in the
>expectation that artificial intelligence would prove to be militarily
>useful. In the 1980s, funders decided to switch their support from
>proposition-based artificial intelligence to connectionism. They did so
>both because of perceived stagnation in the proposition-based approach
>(mainly due to the difficulties mentioned above), and because
>connectionism became more attractive with the discovery (or rediscovery)
>of back-propagation algorithms for training multilayer networks.
>
>More recent developments are described in chapter 13. These include
>virtual-reality systems, attempts to construct societies of artificial
>agents that interact socially, and CYC?a project aimed at explicitly
>representing enough of the commonsense background to enable an
>artificial system to learn more by reading dictionaries, textbooks,
>encyclopedias and newspapers. Chapter 14 is a rich account of
>computational and cognitive neuroscience. Topics touched on include
>challenges to the computational approach, theories of consciousness and
>philosophy of mind. In chapter 15, Boden describes the origins of
>artificial life and then discusses reaction-diffusion equations,
>self-replicating automata, evolutionary networks, computational
>neuro-ethology (computational interpretation of the neural mechanisms
>that underlie the behavior of an animal in its habitat) and work on
>complex systems. Chapter 16 reviews philosophical thinking about mind as
>machine. Is there a mind-body problem? If a robot simulation of a person
>were developed, would it be conscious? Would it suffer from a mind-body
>problem? Would it be alive? A very brief final chapter lists promising
>areas for further research.
>
>This is, as far as I know, the first full-scale history of cognitive
>science. I am sure that knowledgeable readers may have various quibbles
>about one or another aspect of this history (like my own objection above
>to the discussion of Chomsky's work in linguistics). But I doubt that
>many, or in fact any, readers will have the detailed firsthand knowledge
>that Boden has of so much of cognitive science. Future histories of the
>subject will have to build on this one.
>Reviewer Information
>Gilbert Harman is Stuart Professor of Philosophy at Princeton
>University, where in the past he was chair of the Program in Cognitive
>Studies and codirector of the Cognitive Science Laboratory. He is
>coauthor with Sanjeev Kulkarni of Reliable Reasoning: Induction and
>Statistical Learning Theory (The MIT Press, 2007).
>
>Source: American Scientist
>http://www.americanscientist.org/BookReviewTypeDetail/assetid/56418
>