Mike Tintner wrote:
Essentially, Richard & others are replaying the same old problems of
computational explosions - see "computational complexity" in this
history of cog. sci. review - no?
No: this is a misunderstanding of "complexity" unfortunately (cf the
footnote on p1 of my AGIRI paper): computational complexity refers to
how computations scale up, which is not at all the same as the
"complexity" issue, which is about whether or not a particular system
can be explained.
To see the difference, imagine an algorithm that was good enough to be
intelligent, but scaling it up to the size necessary for human-level
intelligence would require a computer the size of a galaxy. Nothing
wrong with the algorithm, and maybe with a quantum computer it would
actually work. This algorithm would be suffering from a computational
complexity problem.
By contrast, there might be proposed algorithms for iimplementing a
human-level intelligence which will never work, no matter how much they
are scaled up (indeed, they may actually deteriorate as they are scaled
up). If this was happening because the designers were not appreciating
that they needed to make subtle and completely non-obvious changes in
the algorithm, to get its high-level behavior to be what they wanted it
to be, and if this were because intelligence requires
complexity-generating processes inside the system, then this would be a
complex systems problem.
Two completely different issues.
Richard Loosemore
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.
click for full image and caption
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
Posted by
Robert Karl Stonjek
Back to top Reply to sender | Reply to group | Reply via web post
Messages in this topic (1)
9. Book Review: Young Minds in Social Worlds - Experience, Meaning, and
Posted by: "Robert Karl Stonjek" [EMAIL PROTECTED] r_karl_s
Tue Dec 11, 2007 3:03 am (PST)
Constructing Cognition
Ethan Remmel
Young Minds in Social Worlds: Experience, Meaning, and Memory. Katherine
Nelson. xiv + 315 pp. Harvard University Press, 2007. $49.95.
The two patron saints of the study of cognitive development (which
involves how thinking and knowledge change with age) are the Swiss
psychologist Jean Piaget and the Russian psychologist Lev Vygotsky. Both
were born in 1896, but Piaget lived and wrote into his 80s, whereas
Vygotsky died of tuberculosis at age 37. Both advocated forms of
constructivism, the theory that children actively construct knowledge,
rather than being passively molded by experience (as in behaviorism) or
programmed by biology (as in nativism). However, Piaget viewed cognitive
development as a product of the individual mind, achieved through
observation and experimentation, whereas Vygotsky viewed it as a social
process, achieved through interaction with more knowledgeable members of
the culture.
Developmental psychologist Katherine Nelson is an apostle of the
Vygotskian approach, known as social constructivism. She is opposed to
the currently popular descendant of the Piagetian approach known as
theory theory, which holds that children construct causal
conceptualizations of different domains of knowledge ("folk theories")
using the same cognitive processes that scientists use to construct
scientific theories (that is, children think like little scientists).
Nelson contends that theory theorists such as Alison Gopnik commit the
psychologist's fallacy-attributing one's own thought processes to
others-and thereby forsake one of constructivism's central insights:
that children's cognition is qualitatively different from that of
adults, not simply quantitatively inferior. In other words, children
don't just know less, they think differently.
In her new book, Young Minds in Social Worlds, Nelson also argues that
computational theories of mind (based on the premise that the mind
operates like a computer) are inadequate, because the ultimate function
of human cognition is not to process abstract information, but to
interpret experience and share cultural meanings with others. She
criticizes evolutionary cognitive psychologists such as Steven Pinker
and nativist developmental psychologists such as Elizabeth Spelke for
allegedly overemphasizing genetic influences on development and
underemphasizing the importance of social and cultural influences. And
Nelson draws on developmental systems theory to describe development as
a complex, interactive, dynamic process in which cognitive structure
gradually emerges, rather than being built into the genes or copied from
the environment.
The bulk of the book reviews cognitive and language development during
the first five years of life, seen from Nelson's theoretical
perspective. Her target audience appears to be other developmental
psychologists, as she refers to many theories and studies without giving
much explanation for readers not already familiar with them.
Developmentalists, however, will not learn much factual information
because she presents no new research. Instead, what they may gain is an
appreciation of Nelson's theoretical perspective on development, which
she calls experiential (to emphasize the importance of the child's
individual experience).
Nelson suggests that a child's consciousness develops in levels that
correspond to the stages proposed by cognitive neuroscientist Merlin
Donald for the evolution of the human mind. Nelson presented this model
in more detail in Language in Cognitive Development (1996), so those who
have read that book will find it familiar.
In Young Minds in Social Worlds, the early chapters on cognitive
development in the infant and toddler ages are uncontroversial and,
frankly, unmemorable. The middle chapters on the acquisition of language
and symbols are much more interesting, perhaps because here Nelson can
draw more on her own research. She is critical of current theories about
word learning that assume that children have the same conceptual
structures as adults and simply need to map the words that they hear
onto the concepts that they already have. Nelson argues convincingly
that children gradually construct concepts through linguistic
interaction. In other words, children do not simply match labels to
pre-existing categories, but rather use linguistic cues to bring their
conceptual boundaries in line with those shared by their linguistic
community.
A corollary of this position is that production may sometimes precede
comprehension (a reversal of the usually assumed sequence): Children may
imitate the use of a word in a particular context and only gradually
acquire a context-independent understanding of the meaning of the word.
In Nelson's view, which is indebted to the psycholinguist Dan Slobin,
language is less than a necessary ingredient for thought itself (that
is, nonlinguistic thought is possible), but more than a neutral vehicle
for the expression of thought. Language is needed for sharing thoughts,
and those thoughts are inevitably shaped by the particular language used.
Nelson raises the interesting question of why children seem to
understand symbolic representations (for example, words, which do not
resemble their referents-onomatopoeia excepted) before they understand
iconic representations (for example, scale models, which do resemble
their referents), even though the former relation, being arbitrary,
would seem harder to comprehend. Although elsewhere she criticizes
nativist explanations ("it's built in"), here she suggests that perhaps
humans have a specific evolved capacity to understand symbols but must
learn to interpret icons. Perhaps, but I think it's more likely that
icons are challenging because, as Judy DeLoache has observed, they have
a dual nature, being both representations and objects in their own
right-for example, a scale model can be interpreted as a toy. This
conflict creates greater demands on executive functions than words do,
because words have no meaning except as representations.
Nelson stumbles a few times in the later chapters on cognitive
development in preschoolers. She writes that "young children tend to
remember the gist of a complex situation and lose track of details." But
in fact, research based on Charles Brainerd and Valerie Reyna's
fuzzy-trace theory finds exactly the opposite: Children rely more on
memory for verbatim details, and adults rely more on memory for gist.
For example, when asked to remember a list of words that cluster around
a theme, adults are more likely to erroneously include a word that is
semantically related to the words supplied, whereas children are more
likely to incorrectly include a word that rhymes with a word actually
presented.
Nelson criticizes research on children's theory of mind (their
understanding of mental states) for overemphasizing one particular
development during the preschool years: understanding of beliefs (that
people can believe different things, including things that are false).
But Nelson then does the same thing herself-saying, for example, that
"competence in representational language is essential for entering the
community of minds (and for solving theory of mind problems)." Nelson
means that the maturation of representational language during the
preschool years enables children to understand beliefs. However, she
neglects the fact that children solve some other theory-of-mind problems
much earlier. For example, 18-month-olds understand desires (that people
want different things).
Nelson also argues that children's understanding of mind is not based on
some sort of underlying theory, because "nothing guarantees that one
person can correctly interpret another." But her argument misses the
mark, because nobody claims that theories are guaranteed to produce
correct interpretations. If theories did, science would be a lot easier!
And despite her antipathy toward theory theory, she writes that
"children's questions indicate that they are mentally instigating
investigations of the causal structure of aspects of the world, both
psychological and physical." That sure sounds to me like they're
theorizing.
Nelson concludes by maintaining that cognitive development is not a
process of individual internalizing of objective knowledge, but rather
is a collaborative interpretation of subjective experience. However, she
eschews the solipsism of postmodern relativism. She sometimes presents
the obvious as profound, in such statements as "this view reflects the
position that cognitive processes are an integral part of memory itself"
and "this view resolves the issue of why thinking in a different
language is different from thinking in a first language; it is different
because the languages are different." She aims for a pragmatic and
balanced account of development, which unfortunately sometimes feels
vague and bland. She may be right, but her book is less fun to read than
those of more provocative writers such as Steven Pinker. For a more
engaging exposition of a similar sociocultural perspective, I recommend
two books by Michael Tomasello: The Cultural Origins of Human Cognition
(1999) and Constructing a Language: A Usage-Based Theory of Language
Acquisition (2003).
Reviewer Information
Ethan Remmel is a cognitive developmental psychologist at Western
Washington University in Bellingham. His research focus is the
relationship between language experience and children's understanding of
the mind.
Source: American Scientist
http://www.americanscientist.org/BookReviewTypeDetail/assetid/56401
Posted by
Robert Karl Stonjek
Back to top Reply to sender | Reply to group | Reply via web post
Messages in this topic (1)
10. Book review: Language, Consciousness, Culture - Essays on Mental Str
Posted by: "Robert Karl Stonjek" [EMAIL PROTECTED] r_karl_s
Tue Dec 11, 2007 3:04 am (PST)
The Functionalist's Dilemma
George Lakoff
Language, Consciousness, Culture: Essays on Mental Structure. Ray
Jackendoff. xxvi + 403 pp. The MIT Press, 2007. $36.
Science, as Thomas Kuhn famously observed, does not progress linearly.
Old paradigms remain as new ones begin to supplant them. And science is
very much a product of the times.
The symbol-manipulation paradigm for the mind spread like wildfire in
the late 1950s. Formal logic in the tradition of Bertrand Russell
dominated Anglo-American philosophy, with W. V. O. Quine as the dominant
figure in America. Formalism reigned in mathematics, fueled by the
Bourbaki tradition in France. Great excitement was generated by the
Church-Turing thesis that Turing machines, formal logic, recursive
functions and Emil Post's formal languages are equivalent. The question
naturally arose: Could thought be characterized as a symbol-manipulation
system?
The idea of artificial intelligence developed out of an attempt to
answer that question, as did the information-processing approach to
cognitive psychology of the 1960s. The mind was seen as computer
software, with the brain as hardware. The software was what mattered.
Any hardware would do: a digital computer or the brain, which was called
wetware and seen (incorrectly) as a general-purpose processor. The
corresponding philosophy of mind, called functionalism, claimed that you
could adequately study the mind independently of the brain by focusing
on the mind's functions as carried out by the manipulation of abstract
symbols.
The time was ripe for Noam Chomsky to adapt the symbol-manipulation
paradigm to linguistics. Chomsky's metaphor was simple: A sentence was a
string of symbols. A language was a set of such strings. A grammar was a
set of recursive procedures for generating such sets. Language was
syntacticized-placed mathematically within a Post system, with abstract
symbols manipulated in algorithmic fashion by precise formal rules.
Because the rules could not look outside the system, language had to be
"autonomous"-independent of the rest of the mind. Meaning and
communication could play no role in the structure of language. The brain
was irrelevant. This approach was called generative linguistics, and it
continues to have adherents in many linguistics departments in the
United States.
In the mid-1970s, another paradigm shift occurred. Neuroscience burst
onto the intellectual stage. Cognitive science expanded beyond formalist
cognitive psychology to include neural models. And cognitive linguistics
emerged, whose proponents (including me) see language and thought not as
an abstract symbol-manipulation system but as physically embodied and
reflecting both the specific properties and the limitations of our
brains and bodies. Cognitive linguistics has been steadily developing
into a rigorously formulated neural theory of language based on
neural-computation theory and actual developments in neuroscience.
Ray Jackendoff's new book, Language, Consciousness, Culture, is set
solidly within the old generative-linguistics paradigm. In it,
Jackendoff staunchly defends functionalism and the symbol-manipulation
paradigm. "Some neuroscientists say we are beyond this stage of inquiry,
that we don't need to talk about 'symbols in the head' anymore. I firmly
disagree," he notes. He goes on to argue that the symbolic
representations given by linguists are simply right, and he takes the
brain to be irrelevant. Interestingly, he does not cite the major work
arguing the opposite, Jerome Feldman's 2006 book, From Molecule to
Metaphor. Feldman shows how the analyses of language and thought done by
cognitive linguists can be characterized in terms of neural computation.
But, as Jackendoff says, "Cognitive Grammarians . . . have been
steadfastly ignored by mainstream generative linguistics." Just as Kuhn
would have predicted.
All this creates a dilemma for Jackendoff. He sees the limitations of
the functionalist paradigm and rails correctly against Chomsky's
syntacticization of meaning, but he stays with a version of symbolic
logic, in which meaning is also syntacticized by a formal logical syntax.
Jackendoff has read widely in cognitive science and neuroscience, while
"steadfastly ignoring" the literature of cognitive and neural theories
of language, which answers many of the questions he raises, although in
a paradigm he refuses to consider. He sees correctly that the cognitive
and brain sciences ought to be taken seriously by philosophers and
social scientists, but his forays into social, moral and political ideas
are limited by his functionalist approach.
Take the question of meaning. In 1963, I proposed a theory of generative
semantics in which a version of formal logic became an input to
generative grammars. I was later joined in this enterprise by James D.
McCawley and John Robert Ross, two of Chomsky's best-known students.
Among our tenets were that conceptual structure is generative, that it
is prior to and independent of language, and that it is inaccessible to
consciousness. Jackendoff argued strongly against this position at the
time, but in this book, only 40 years later, he accepts these tenets,
while keeping Chomsky's idea that syntactic structure is independent of
meaning. Jackendoff adopts a parallel-structure theory in which he holds
both ideas at once. As we did then, he now declares that Chomsky's
syntactocentrism is a "scientific mistake." Yet, as a Chomskyan
syntactician, he has to keep a version of the "scientific mistake"-an
autonomous syntax for grammar alongside his autonomous syntax for
meaning (a kind of symbolic logic).
In the 1960s, Charles J. Fillmore proposed a theory of "case grammar" in
which there were semantic roles (agent, patient, experiencer and so on)
and principles mapping these roles to grammar. This idea was accepted in
cognitive linguistics and has been developed over the past 40 years by
Fillmore and many others in the theory of grammatical constructions, in
which semantics is directly paired with syntactic form. Jackendoff
adopts a version of this theory without mentioning Fillmore. Laudable,
if a little late.
In 1975, Fillmore began the development of "frame semantics," expanding
the notion in great detail over the next three decades. Conceptual
framing has become central in cognitive linguistics worldwide and is
widely applied, as in my work on political analysis over the past
decade. Jackendoff accepts a much less precise and less worked-out
version of frames set forth by Erving Goffman and Marvin Minsky in the
mid-1970s, but he "steadfastly ignores" Fillmore's elaborate research
and its widespread application.
In 1997, Srini Narayanan, in his dissertation at the University of
California, Berkeley, worked out a neural computational account of
actions and events, which generalizes to the semantics of aspect (event
structure) in linguistics and actually computes the logic of aspect. In
Language, Consciousness, Culture, Jackendoff tries to adapt Chomsky's
syntactic structures to action structure, which Patricia Greenfield of
UCLA first attempted in the 1960s. Jackendoff's account, coming a decade
after Narayanan's, doesn't characterize actions nearly as well, does not
compute the logic of actions, does not characterize the semantics of
aspect and does not fit the theory of neural computation. But it is
gratifying to see Jackendoff trying to link motor actions to linguistics
(as Chomsky never would), in an attempt to break out of the
functionalist mold without leaving it.
Jackendoff is asking questions well beyond the Chomskyan enterprise, and
in some cases he approaches what cognitive linguists have achieved. But
one place he gets it very wrong is conceptual metaphor.
Mark Johnson and I wrote Metaphors We Live By (1980) almost three
decades ago. Since then hundreds of researchers have developed a whole
field of study around the subject. In our 1999 book Philosophy in the
Flesh, Johnson and I elaborated in great detail on Narayanan's neural
computational theory of metaphor.
In the neural theory, conceptual metaphor arises in childhood when
experiences regularly occur together, activating different brain
regions. Activation repeatedly spreads along neural pathways,
progressively strengthening synapses in pathways between those brain
regions until new circuitry is formed linking them. The new circuitry
physically constitutes the metaphor, carrying out a neural mapping
between frame circuitry in the regions and permitting new inferences.
The conceptual metaphor MORE IS UP (as in "prices rose," "the
temperature fell") is learned because brain regions for quantity and
verticality are both activated whenever you pour liquid into a glass or
build any pile. AFFECTION IS WARMTH (as in "She's a warm person," or
"She's an ice queen") because when you are held affectionately as a
child by your parents, you feel physical warmth. Hundreds of such
primary metaphors are learned early in life. Complex metaphors are
formed by neural bindings of these primary metaphors. And metaphorical
language expresses both primary and complex metaphors.
Because we first experience governance within the family, one widespread
primary metaphor is A GOVERNING INSTITUTION IS A FAMILY, with authority
based on parental authority. Within the literal Family Frame, rights and
obligations arise from what is allowed and required, given the desires
and responsibilities of parents and children. Children want to be fed
and taken care of, and parents are required to provide for them.
Children have other desires that may be allowed or forbidden. Parents
may require certain things of children.
Under the metaphor A GOVERNING INSTITUTION IS A FAMILY, what is required
by an authority is called an obligation, and what is allowed or has to
be provided by an authority is called a right. The metaphor applies at
various levels, so there are higher governing institutions, such as
societies, nature or the universe, and metaphorical authorities, such as
social or moral norms, natural laws and God. At each level, the logic of
family-based authority is metaphorically duplicated for rights and
obligations, with authorities at a lower level subjected to authority at
a higher level. No special metaphors unique to rights and obligations
are needed. Other independently existing primary metaphors flesh out the
complexities: Because ACHIEVING A DESIRED PURPOSE IS GETTING A DESIRED
OBJECT, rights are seen as metaphorical possessions, which can be given
to you, held onto or lost. Because requirements can be difficult and
DIFFICULTIES ARE BURDENS, we speak of "taking on" or "undertaking"
obligations.
You would never know any of this from reading Jackendoff's brief
discussion of whether rights and obligations are understood
metaphorically. He reaches the conclusion he has to reach: that no
conceptual metaphor at all is used in understanding rights and
obligations. This is not surprising, because typically he has largely
ignored the cognitive linguistics literature.
Had the discussion of rights and obligations in Language, Consciousness,
Culture appeared in the late 1960s, it would have been seen as
excellent. But coming out nearly 40 years later, it is inadequate,
because it fails to explain why we reason about rights and obligations
as we do, both in the West and elsewhere in the world. The
neural-metaphorical understanding gives a correct account of the data
plus an explanation grounded in biology. Such explanations are lacking
throughout the book because Jackendoff still holds to functionalism.
For a cognitive linguist like myself, reading Jackendoff's book is both
painful and hopeful-painful because he keeps trying to do interesting
and important intellectual work while being stuck in a paradigm that
won't allow it, and hopeful because he may help the transition from a
brain-ignoring symbol-manipulation paradigm to a brain-based neural
theory of thought and language. I wish that other linguists, both
generative and cognitive, had his scope and intellectual ambition.
Reviewer Information
George Lakoff is Richard and Rhoda Goldman Distinguished Professor of
Cognitive Science and Linguistics at the University of California,
Berkeley, and Senior Fellow at the Rockridge Institute. His recent books
include Moral Politics (University of Chicago Press, 1996 and 2002),
Don't Think of an Elephant! (Chelsea Green Publishing, 2004), Whose
Freedom? (Picador, 2006), and Thinking Points (Farrar, Straus & Giroux,
2006), with the Rockridge Institute. The Political Mind will appear from
Viking/Penguin in 2008.
Source: American Scientist
http://www.americanscientist.org/BookReviewTypeDetail/assetid/56419
Posted by
Robert Karl Stonjek
Back to top Reply to sender | Reply to group | Reply via web post
Messages in this topic (1)
11a. Book Review: The First Word - The Search for the Origins of Language
Posted by: "Robert Karl Stonjek" [EMAIL PROTECTED] r_karl_s
Tue Dec 11, 2007 3:04 am (PST)
Not the Last Word
Michael C. Corballis
The First Word: The Search for the Origins of Language. Christine
Kenneally. x + 357 pp. Viking, 2007. $26.95.
In 1866, the Linguistic Society of Paris famously banned all discussion
of the origins of language. The London Philological Society followed
suit in 1872. Speculation about the evolution of language remained
stifled for a century, and it was only in the 1970s that muted
discussion began to emerge, often with an air of apology. Eventually,
though, the floodgates opened, and the past two decades have seen a
deluge of articles, books and conferences on the topic. The current
state of the field is largely one of chaos, to the point that some
observers might be tempted to think the ban should be reinstated. Most
agree that language is in essence uniquely human, so that evidence as to
its evolution remains indirect, and speculation can run wild.
Nevertheless, recent advances in genetics, archeology, neurophysiology
and computer modeling have provided powerful if sometimes conflicting
leads.
Christine Kenneally reviews the current state of the field in her new
book. An experienced science journalist with a Ph.D. in linguistics, she
is well qualified for the task. The focal point for her discussion is a
high-profile symposium on the evolution of language held in 2005 at
Stony Brook, New York, where many of the leading players met and gave
talks, but her reading and interviews range more widely. The First Word
is almost certainly not the last word, but it does provide a lucid,
readable, comprehensive account of the different ideas that are now
current.
One figure who continues to exert a major if not always benign influence
is Noam Chomsky, the dominant linguist of the past half-century, who
made a rare appearance at the symposium. It might be said that Chomsky
actually helped prolong the ban, because he has long argued that one
simply cannot know how language evolved and has even suggested that
language may not be the product of natural selection. This position was
first explicitly questioned by Steven Pinker and Paul Bloom (who were
not present at the symposium but are appropriately included in
Kenneally's story) in a classic article published in 1990, in which they
retained much of the Chomskyan stance but made a strong case for the
incremental evolution of language through natural selection. Much of the
subsequent development has been in more direct opposition to Chomsky and
seems set to redefine the nature of language itself. As the book
relates, Chomsky appeared at the symposium only to give a public
address, which I and many of the others in attendance found largely
incomprehensible; he arrived and departed without engaging with any of
the other speakers.
Chomsky first achieved prominence with his 1957 book Syntactic
Structures, which argued that syntax could not be explained in terms of
associations. However, theorists such as Simon Kirby and Morten
Christiansen have made considerable progress toward developing
connectionist theories; language may after all depend on learning
principles, and not on some innate language-acquisition device. Sue
Savage-Rumbaugh is accorded her rightful place as a pioneer in the
effort to discover the linguistic abilities of apes, but the notion of a
continuity between ape language and human language remains implacably
opposed by those who retain at least vestiges of Chomskyan theory, a
group that includes Steven Pinker, Ray Jackendoff and Derek Bickerton.
Kenneally also describes the idiosyncratic work of Luc Steels, who has
established artificial robot-inhabited worlds in which languagelike
structures arise spontaneously. As Jim Hurford, another prominent
symposiast, remarked, we may be witnessing the demise of the Chomskyan
notion of universal grammar, the supposedly innate structure, unique to
humans and peculiar to language itself, that is said to underlie all
languages. Instead, language may depend on more general cognitive
abilities.
One complaint I have is that most of the experts discussed in the book
seem to equate language with speech. (Kenneally herself writes that
speech "is crucial to language.") A partial exception is Michael Arbib,
who grounds language evolution in manual gestures, building a scenario
in which an intermediate form of communication, which he calls
proto-sign, forms the scaffold for the incorporation of vocalization in
an ascending spiral toward full syntactic speech. Arbib draws on
so-called mirror neurons, first discovered in the monkey brain, which
respond both when the subject makes grasping movements and when it
observes the same movements made by others. These neurons are found in
areas homologous to speech areas in the human brain and seem to provide
a natural platform for the evolution of language. Mirror neurons,
though, may be an overworked commodity in modern evolutionary and
cognitive theory, providing convenient explanations for anything from
language to imitation to theory of mind.
Yet language can consist of a combination of hand gestures and facial
expressions rather than vocalizations. Even Arbib appears not to
recognize that signed languages are indeed true languages; he seemingly
confuses them with pantomime. My own view, which I presented at the
symposium and which is mentioned in the book, is in fact similar to
Arbib's but allows that language might well have evolved as a
sophisticated form of ritualized and grammaticalized gesture before the
eventual takeover by vocalization; even now manual gestures are woven
into our speech. Arbib is quoted as saying "It's hard to build up a rich
tradition just through gesture," but a visit to Gallaudet University in
Washington, D.C., where the language of instruction is American Sign
Language, might persuade him otherwise. Missing from The First Word are
sign-language experts such as Ursula Bellugi, Karen Emmorey, David F.
Armstrong, Sherman Wilcox and the late William C. Stokoe.
Given the book's breadth of coverage, such an omission is all the more
surprising. But that is perhaps a minor quibble. Kenneally's dilemma is
that, although she found herself excited by the ideas she encountered,
many of them are mutually incompatible, so no clear pattern
emerges.Nevertheless, she writes in an engaging, chatty style, and
readers will gain a broad understanding of what language is about and
how it might have evolved. She ends in a rather gimmicky fashion by
asking various researchers their opinion as to whether and how language
would evolve in a boatload of babies shipwrecked on the Galápagos
Islands but provided with the sustenance to survive and thrive. My
response? They might well develop language, but they'd surely all have
different theories as to how it happened.
Reviewer Information
Michael C. Corballis is professor of psychology at the University of
Auckland and is the author of, among other books, From Hand to Mouth:
The Origins of Language (Princeton University Press, 2002) and The
Lopsided Ape: Evolution of the Generative Mind (Oxford University Press,
1991).
Source: American Scientist
http://www.americanscientist.org/BookReviewTypeDetail/assetid/56421
-----
This list is sponsored by AGIRI: http://www.agiri.org/email
To unsubscribe or change your options, please go to:
http://v2.listbox.com/member/?member_id=8660244&id_secret=74563363-597e20
