| Title & Author | Abstract | |
|---|---|---|
| 9(04) | ARE CONNECTIONIST MODELS THEORIES OF COGNITION? Target Article by Green on Connectionist Explanation Christopher D. Green Department of Psychology York University North York, Ontario M3J 1P3 CANADA http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
This paper explores the question of whether connectionist
models of cognition should be considered to be scientific theories
of the cognitive domain. It is argued that in traditional
scientific theories, there is a fairly close connection between the
theoretical (unobservable) entities postulated and the empirical
observations accounted for. In connectionist models, however,
hundreds of theoretical terms are postulated -- viz., nodes and
connections -- that are far removed from the observable phenomena.
As a result, many of the features of any given connectionist model
are relatively optional. This leads to the question of what,
exactly, is learned about a cognitive domain modelled by a
connectionist network.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(05) | DO WIRES MODEL NEURONS?
Commentary on Green on Connectionist-Explanation Jack Orbach Department of Psychology Queens College City University of New York Flushing, NY 11367 JOrbach@worldnet.att.net |
Abstract:
Connectionists should not lose sight of the fact that the
electronic circuit has little in common with the neural circuit in
the brain.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(06) | THE ROLE OF IMPLEMENTATION IN CONNECTIONIST EXPLANATION
Commentary on Green on Connectionist-Explanation Gerard J. O'Brien Department of Philosophy University of Adelaide South Australia 5005 Australia http://chomsky.arts.adelaide.edu.au/Philosophy/gobrien/gobrien.htm gobrien@arts.adelaide.edu.au |
Abstract:
Green is right to question the explanatory role of
connectionist models in cognitive science. What is more, he is
generally right in his judgement that the only way of interpreting
connectionist models as theories of cognitive phenomena is by
construing them as "literal models of brain activity" (1998, para.
20). This is because connectionist explanations of cognitive
phenomena are more dependent on details of implementation than
their conventional ("classical") counterparts.
Keywords: classicism, cognition, connectionism, explanation, implementation, methodology, theory. |
| 9(07) | LASHLEY'S LESSON IS NOT GERMANE
Reply to Orbach on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Orbach (1998) incorrectly interprets my target article
(Green 1998) as claiming that connectionist networks actually model
neural activity, whereas in reality I argue that nets will NEED to
model neural activity if they are to model anything at all.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(08) | PROBLEMS WITH THE IMPLEMENTATION ARGUMENT:
Reply to O'Brien on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
O'Brien (1998) and I (Green 1998) agree on many issues,
but his reliance on Clark's (1990, 1993) justification for
connectionist research in terms of explanatory inversion raises as
many problems as it solves. Connectionism seems to generate
ontological problems that do not impede symbolic cognitive
science.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(09) | ARE HYPOTHETICAL CONSTRUCTS PREFERRED OVER INTERVENING VARIABLES?
Commentary on Green on Connectionist-Explanation Michael E. Young Dept. of Psychology The University of Iowa Iowa City, IA 52242 www.psychology.uiowa/edu/faculty/young.htm michael-e-young@uiowa.edu |
Abstract:
Green (1998) expresses dissatisfaction with contemporary
connectionist models as theories of cognition. A reexamination of
the historical distinction between hypothetical constructs and
intervening variables and their relative roles in theory
development reveals an important role for well-designed,
parsimonious connectionist models in the study of cognition.
Although realist theories (i.e., theories that include hypothetical
constructs) are bolder and might provide more intellectual
satisfaction to psychologists, instrumentalist theories (i.e.,
theories that include only intervening variables) can bring rigor
and understanding to the enterprise of cognitive science.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(10) | LOCALIST CONNECTIONISM FITS THE BILL
Commentary on Green on Connectionist-Explanation Jonathan Grainger Centre de Recherche en Psychologie Cognitive, CNRS Universite de Provence Aix-en-Provence France Arthur M. Jacobs Dept. of Psychology Philips University of Marburg, Marbug, Germany grainger@newsup.univ-mrs.fr jacobsa@mailer.uni-marburg.de |
Abstract:
Green (1998) restates a now standard critique of
connectionist models: they have poor explanatory value as a result
of their opaque functioning. However, this problem only arises in
connectionist models that use distributed hidden unit
representations, and is NOT a feature of localist connectionism.
Indeed, Green's critique reads as an appeal for the development of
localist connectionist models as an excellent starting point for
building a unified theory of human cognition.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(11) | CONNECTIONISM AND COGNITIVE THEORIES
Commentary on Green on Connectionist-Explanation David A. Medler Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh, PA 15213 Michael R. W. Dawson Biological Computation Project Department of Psychology University of Alberta Edmonton, Alberta Canada T6G 2E9 medler@cnbc.cmu.edu mike@psych.ualberta.ca |
Abstract:
The relationship between connectionist models and
cognitive theories has been a source of considerable debate within
cognitive science. Green (1998) has recently joined this debate,
arguing that connectionist models should only be interpreted as
literal models of brain activity; in other words, connectionist
models only contribute to cognitive theories at the
implementational level. Recent results, however, have shown that
interpreting the internal structure of connectionist models can
produce novel cognitive theories that are more than mere
implementations of classical theories (e.g., Dawson, Medler, &
Berkeley, 1997). Furthermore, such connectionist theories have an
advantage over more classical approaches to cognitive theories in
that they posit explanatory -- as opposed to merely descriptive --
theories of cognition.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(12) | ARE NEURAL NETS A VALID MODEL OF COGNITION?
Commentary on Green on Connectionist-Explanation William C. Hoffman Institute for Topological Psychology 2591 W. Camino Llano, Tucson, AZ, USA 85742-9074 willhof@worldnet.att.net |
Abstract:
Connectionist models purport to model cognitive
neuropsychology by means of adaptive linear algebra applied to
point neurons. As a theory of cognition, this approach is deficient
in several aspects: noncovergence in neurobiological real-time;
omission of two topological structures fundamental to the
information processing psychology on which connectionist models are
based; omission of the local structure of neurobiological
processing; omission of actual neuron morphologies, cortical
cytoarchitecture, and the cortical orientation response; the
inability to perform memory retrieval from point-neuron "weights"
in neurobiological real-time; and failure to implement
psychological constancy. Cognitive processing by neuronal flows is
offered as a viable alternative. Finally, neural nets fail Hempel's
test of empirical and systematic import.
Keywords: connectionism, neural nets, neuropsychology, cognition, perception, computational models, philosophy of science, memory, psychological constancy, symmetric difference. |
| 9(13) | REALISM, INSTRUMENTALISM AND CONNECTIONISM
Reply to Young on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Young (1998) argues that my critique of connectionism
(Green 1998) is grounded in an assumption that realism is superior
to instrumentalism as an interpretation of scientific theories, and
that the difficulties that I argue connectionism faces can be
avoided if connectionists adopt an instrumentalist stance. I made
no such assumption, however, and a closer examination of
instrumentalism shows it to be detrimental to the connectionist
cause. Realism -- probably neural realism -- remains the
connectionist's best hope.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(14) | DOES LOCALISM SOLVE CONNECTIONISM'S PROBLEM?
Reply to Grainger & Jacobs on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Grainger & Jacobs (1998) argue that the problems facing
connectionism discussed in my target article (Green 1998) can be
overcome by switching to a localist connectionist perspective. I
question whether the cost of doing so outweighs the disadvantages
of staying with the parallel distributed processing approach to
connectionist cognitive science.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(15) | STATISTICAL ANALYSES DO NOT SOLVE CONNECTIONISM'S PROBLEM
Reply to Medler & Dawson on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Medler & Dawson (1998) claim (1) that I am just a closet
implementationalist, (2) that I have ignored a range of statistical
analyses that answer my challenge to connectionism, and (3) that
only connectionist networks can produce explanatory models of
cognition. I reply that I am not an implementationalist, that the
statistical analyses to which they refer do not solve the problem I
have posed, and that the question of whether a theory is
explanatory is independent of the question of how it was generated.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(16) | OF NEURONS AND CONNECTIONIST NETWORKS
Reply to Hoffman on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Hoffman (1998) tells us of a number of difficulties
connectionists may face in any attempt to model neural activity
with connectionist networks. I have no reason to doubt that, but it
is more of a problem for connectionists than it is for my argument
(Green 1998).
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(17) | WHY CONNECTIONIST NETS ARE GOOD MODELS
Commentary on Green on Connectionist-Explanation Changsin Lee, Bram van Heuveln, Clayton T. Morrison & Eric Dietrich PACCS, Department of Philosophy Binghamton University Binghamton, NY 13902 http://www.paccs.binghamton.edu/chang/ chang@turing.paccs.binghamton.edu |
Abstract:
We agree with Green that some connectionists do not make
it clear what their nets are modeling. However, connectionism is
still a viable project, connectionism, because it provides a
different ontology and different ways of modeling cognition by
requiring us to consider implementational details. We also argue
against Green's view of models in science and his characterization
of connectionist networks.
Keywords: cognition, connectionism, explanation, model, ontology, theory |
| 9(18) | CONNECTIONIST MODELING AND THEORIZING:
WHO DOES THE EXPLAINING AND HOW? Commentary on Green on Connectionist-Explanation Morris Goldsmith Department of Psychology University of Haifa Haifa, 31905, Israel mgold@psy.haifa.ac.il |
Abstract:
Green's (1998) criticism that connectionist models are
devoid of theoretical substance rests on a simplistic view of the
nature of connectionist models and a failure to acknowledge the
division of labor between the model and the modeller in the
enterprise of connectionist modelling. The "theoretical terms" of
connectionist theory are not to be found in processing units or in
connections but in more abstract characterizations of the
functional properties of networks. Moreover, these properties are
-- and at present should be -- only loosely tied to the known (and
largely unknown) properties of neural networks in the brain.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(19) | DOES BRAIN ACTIVITY-ORIENTED MODELLING SOLVE THE PROBLEM?
Commentary on Green on Connectionist-Explanation Claus Lamm Brain Research Laboratory Department of Psychology University of Vienna A-1010 Vienna Austria Claus.Lamm@univie.ac.at |
Abstract:
Claiming that it is not clear how many theoretical terms
a connectionist model has to be built of is one of Green's (1998a)
main arguments for referring to (a) a lack of correspondence of the
theoretical entities of connectionist models to any type of
empirical entity and (b) the resulting abundance of degrees of
freedoms in the connectionist modelling of cognition. A more
brain-oriented modelling approach might yield the desired
theoretico-empirical mapping but it does not reduce a model's
degrees of freedom.
Keywords: cognition, connectionism, methodology, theory, computer modelling, epistemology |
| 9(20) | COGNITIVE THEORY AND NEURAL MODEL: THE ROLE OF LOCAL REPRESENTATIONS
Commentary on Green on Connectionist-Explanation Paul A. Watters Department of Computing Macquarie University NSW 2109 AUSTRALIA pwatters@mpce.mq.edu.au |
Abstract:
Green raises a number of questions regarding the role of
"connectionist" models in scientific theories of cognition, one of
which concerns exactly what it is that units in artificial neural
networks (ANNs) stand for, if not specific neurones or groups of
neurones, or indeed, specific theoretical entities. In placing all
connectionist models in the same basket, Green seems to have
ignored the fundamental differences which distinguish classes of
models from each other. In this commentary, we address the issue of
distributed versus localised representations in ANNs, arguing that
it is difficult (but not impossible) to investigate what units
stand for in the former case, but that units do correspond to
specific theoretical entities in the latter case. We review the
role of localised representations in a neural network model of a
semantic system in which each unit corresponds to a letter, word,
word sense, or semantic feature, and whose dynamics and behaviour
match those predicted from a cognitive theory of skilled reading.
Thus, we argue that ANNs might be useful in developing general
mathematical models of processes for existing cognitive theories
that already enjoy empirical support.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(21) | FUNCTION, SUFFICIENTLY CONSTRAINED, IMPLIES FORM
Commentary on Green on Connectionist-Explanation Robert M. French Psychology Department (B33) University of Liege, 4000 Liege, Belgium http://www.fapse.ulg.ac.be/Lab/Trav/rfrench.html Axel Cleeremans Seminaire de Recherche en Sciences Cognitives Universite Libre de Bruxelles 1050 Brussels, Belgium rfrench@ulg.ac.be axcleer@ulb.ac.be |
Abstract:
Green's (1998) target article is an attack on most
current connectionist models of cognition. Our commentary will
suggest that there is an essential component missing in his
discussion of modeling, namely, the idea that the appropriate
level of the model needs to be specified. We will further suggest
that the precise form (size, topology, learning rules, etc.) of
connectionist networks will fall out as ever more detailed
constraints are placed on their function.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(22) | MODELS AND THEORIES OF COGNITION ARE ALGORITHMS
Commentary on Green on Connectionist-Explanation Bruce Bridgeman Department of Psychology University of California Santa Cruz, CA 95064 USA bruceb@cats.ucsc.edu |
Abstract:
PDP models (sometimes misnamed "connectionist") solve
computational problems with a family of algorithms, but changeable
weights between their connections mean that the details of their
algorithms are subject to change. Thus they do not fulfill the
requirement that a model must specify its algorithm for solving a
computational problem, or that it must model real data and fail to
model false data. Other models use distributed coding but retain
homeomorphism and explicit algorithms. An example uses a lateral
inhibitory network with fixed weights to model visual masking and
sensory memory.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(23) | CONNECTIONIST NETS ARE ONLY GOOD MODELS IF WE KNOW WHAT THEY MODEL
Reply to Lee et al. on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Lee, Van Heuveln, Morrison, & Dietrich (1998) suggest,
incorrectly, that I argued (Green 1998a) that connectionist
networks will not be scientific models unless and until they
capture every aspect of neural activity. What I argued was that
unless and until connectionists come to terms with the idea that
connectionist networks must model SOMETHING (and neural activity
currently seems to be the best candidate, but it need not be the
only one) they are not models of anything at all, and therefore may
have little role to play in cognitive science.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(24) | CAN CONNECTIONIST THEORIES ILLUMINATE COGNITION?
Comment on Green on Connectionist-Explanation Athanassios Raftopoulos Assistant Professor of Philosophy and Cognitive Science American College of Thessaloniki Anatolia College P.O. BOX 21021, 55510 PYLEA Thessaloniki, GREECE maloupa@compulink.gr |
Abstract:
In this commentary I attempt to show in what sense we can
speak of connectionist theory as illuminating cognition. It is
usually argued that distributed connectionist networks do not
explain brain function because they do not use the appropriate
explanatory vocabulary of propositional attitudes, and because
their basic terms, being theoretical, do not refer to anything.
There is a level of analysis, however, at which the propositional
attitude vocabulary can be reconstructed and used to explain the
performance of networks; and the basic terms of networks are not
theoretical but observable entities that purport to refer to terms
used to describe the brain.
Keywords: connectionism, cognition, explanation, philosophy of science, theory, theoretical terms. |
| 9(25) | HIGHER FUNCTIONAL PROPERTIES DO NOT SOLVE CONNECTIONISM'S PROBLEMS
Reply to Goldsmith on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Goldsmith (1998) argues that I (Green 1998a) am wrong
in asserting that nodes and connections are the theoretical
entities of connectionist theories. I reply that if he is right,
then connectionist theory is not connectionist after all. I also
comment briefly on Seidenberg's (1993) approach to the
interpretation of connectionist research, and on the issue of the
proper distinction to be drawn between theories and models.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(26) | THE DEGREES OF FREEDOM WOULD BE TOLERABLE IF NODES WERE NEURAL
Reply to Lamm on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Lamm (1998) expresses concern that there is a lack of fit
between my call to connectionists to declare themselves to be
direct modelers of neural activity and my concern that
connectionist nets have too many degree of freedom (Green 1998). I
am sympathetic with his worry, but argue that the degrees of
freedom problem does not loom as large once we know what
constraints we are working under -- as we would if we declared that
connectionist nets are literal neural models.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(27) | LOCALIST CONNECTIONISM DOES NOT ADDRESS THE ISSUE
Reply to Watters on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Watters (1998) is concerned that I excluded localist
connectionist networks from consideration (Green 1998a). As
indicated in my prior reply (Green 1998b), localist nets do not
suffer from the problems outlined in the target article because
they are a species of "classical" symbolic cognitive theory.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(28) | SEMANTICS IS NOT THE ISSUE
Reply to French & Cleeremans on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
French & Cleeremans claim that my argument (Green 1998)
requires that every part of a connectionist network be semantically
interpretable. They have confused semantic interpretation (an issue
peculiar to cognitive science) with a simple correspondence between
aspects of models and aspects of the portion of the world being
modeled (an issue as relevant to physics as to cognitive science),
and have thereby misunderstood my position. Most of the rest of
their commentary follows from their initial misapprehension.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(29) | LATERAL INHIBITION IS A GOOD EXAMPLE
Reply to Bridgeman on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo/ christo@yorku.ca |
Abstract:
Bridgeman's (1998) example of a class of networks that
are grounded in the known neuroanatomy of the Limulus addresses
many of the problems I raised quite nicely. I also discuss the
differences between the terms "connectionist," "PDP," and "neural
network."
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(30) | CONNECTIONIST MODELLING STRATEGIES
Commentary on Green on Connectionist-Explanation Jonathan Opie Department of Philosophy The University of Adelaide South Australia 5005 http://chomsky.arts.adelaide.edu.au/Philosophy/jopie/jopie.htm jopie@arts.adelaide.edu.au |
Abstract:
Green offers us two options: either connectionist models
are literal models of brain activity or they are mere instruments,
with little or no ontological significance. According to Green,
only the first option renders connectionist models genuinely
explanatory. I think there is a third possibility. Connectionist
models are not literal models of brain activity, but neither are
they mere instruments. They are abstract, IDEALISED models of the
brain that are capable of providing genuine explanations of
cognitive phenomena.
Keywords: connectionism, explanation, instrumentalism, realism, idealisation. |
| 9(35) | NEITHER SEMANTICS NOR THEORY-OBSERVATION ARE RELEVANT
Reply to Raftopoulos on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Raftopoulos (1998) claims that I (Green 1998) argued that
every node in a connectionist network must be semantically
interpretable, and that I rely crucially on an untenable
distinction between theory and observation to make my argument run.
I reply by showing that neither of these claims is correct, and
that Raftopoulos's case against my argument does not appear to be
coherent.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(36) | CONNECTIONISM IS A PROGRESSIVE RESEARCH PROGRAMME:
"COGNITIVE" CONNECTIONIST MODELS ARE JUST MODELS Commentary on Green on Connectionist-Explanation Michael Thomas Department of Psychology King Alfred's University College Winchester Hants SO22 4NR Tony Stone Division of Psychology South Bank University London SE1 0AA michael.thomas@psy.ox.ac.uk stonea@sbu.ac.uk |
Abstract:
Connectionist models are cognitive models which can serve
two functions. They can demonstrate the computational feasibility
of a cognitive theory (in this sense they model cognitive
theories), or they can suggest new ways of conceiving the
functional structure of the cognitive system. The latter leads to
connectionist theories with new theoretical constructs, such as
stable attractors, or soft constraint satisfaction. A number of
examples of connectionist models and theories demonstrate the
fertility of connectionism, a progressive research programme in
Lakatos's (1970) sense. Green's (1998a) specificity argument
against connectionist theoretical constructs fails because it
relies upon a simplistic view of theoretical constructs that would
undermine even the "gene" construct, Green's paradigmatic example
of a theoretical entity in good standing. This view of theoretical
entities is based upon a simplistic Popperian picture of science.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(37) | IDEALISATION IS FINE; OPPORTUNISM IS NOT
Reply to Opie on Connectionist-Explanation Christopher D. Green Department of Psychology York University Toronto, Ontario M3J 1P3 Canada http://www.yorku.ca/faculty/academic/christo christo@yorku.ca |
Abstract:
Opie (1998) seems to accept my worries about the
ontological base of connectionism and resolves them by accepting my
suggestion that neurology is the ground of connectionist cognitive
science. He goes on to argue that scientists should be given some
room to idealise the entities under study. One cannot agree.
However, he seems to begs the question in suggesting that units and
connections are the very things that units and connections model.
There also appear to be some problems with the use of sources in
support of his position.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |
| 9(47) | CONNECTIONIST MODELS CAN REVEAL GOOD ANALOGIES
Commentary on Green on Connectionist-Explanation Alberto Greco DISA Psychology Laboratory University of Genoa Genova, Italy http://www.lettere.unige.it/sif/strutture/1/home/greco/index.htm greco@disa.unige.it |
Abstract:
Green (1998a) argues that distributed connectionist
models are not theories of cognition. This is reasonable
if it means that the explanatory role of connectionist models
is not clear, but Green's analysis seems directed against the wrong
target when he applies a realist position to models. His argument
confuses models with objects. Models are useful as long as they
establish analogies between unknown and known phenomena; but not
all details are important. The real problem may concern the
explanatory role of connectionist models (which is what Green also
seems concerned about), but then it should be formulated on
different grounds. If they are intended as cognitive models (and
not as mere AI artifacts), their internal operations should be
describable (by analogy) using a cognitive vocabulary. This is
often not the case with connectionist models. Are they always
useless as cognitive models then? I cannot share Green's conclusion
that the only hope for connectionism is to model brain activity.
On the contrary, because the most attractive feature of
connectionist models is that they can perform cognitive tasks using
no symbols, they can be useful tools for studying (by analogy) the
origin and grounding of symbols.
Keywords: artificial intelligence, cognition, computer modelling, connectionism, epistemology, explanation, methodology, neural nets, philosophy of science, theory. |