Learning/Representation: BBS Call for Commentators

[Stevan Harnad]

    Below is the abstract of a forthcoming target article on:

                COMPUTATION, REPRESENTATION AND LEARNING
                by Andy Clark and Chris Thronton

This article has been accepted for publication in Behavioral and Brain
Sciences (BBS), an international, interdisciplinary journal providing
Open Peer Commentary on important and controversial current research in
the biobehavioral and cognitive sciences.

Commentators must be current BBS Associates or nominated by a current
BBS Associate. To be considered as a commentator for this article, to
suggest other appropriate commentators, or for information about how to
become a BBS Associate, please send email to:

    bbs at soton.ac.uk          or write to:

    Behavioral and Brain Sciences
    Department of Psychology
    University of Southampton
    Highfield, Southampton
    SO17 1BJ UNITED KINGDOM
    http://www.princeton.edu/~harnad/bbs.html
    gopher://gopher.princeton.edu:70/11/.libraries/.pujournals
    ftp://ftp.princeton.edu/pub/harnad/BBS
    
To help us put together a balanced list of commentators, please give
some indication of the aspects of the topic on which you would bring
your areas of expertise to bear if you were selected as a commentator.
An electronic draft of the full text is available for inspection by
anonymous ftp (or gopher or world-wide-web) according to the
instructions that follow after the abstract.
____________________________________________________________________


        TRADING SPACES: COMPUTATION, REPRESENTATION AND THE LIMITS
                      OF UNINFORMED LEARNING
 
                    Andy Clark
                    Philosophy/Neuroscience/Psychology Program,
                    Washington University in St Louis,
                    Campus Box 1073,
                    St Louis, MO-63130, USA
                    andy at twinearth.wustl.edu
 
                    Chris Thornton,
                    Cognitive and Computing Sciences,
                    University of Sussex,
                    Brighton, BN1 9QH, UK
                    Chris.Thornton at cogs.sussex.ac.uk
 
    KEYWORDS: Learning, connectionism, statistics, representation, search
 
    ABSTRACT: Some regularities enjoy only an attenuated existence
    in a body of training data. These are regularities whose
    statistical visibility depends on some systematic re-coding of
    the data. The space of possible re-codings is, however,
    infinitely large - it is the space of applicable Turing
    machines.  As a result, mappings which pivot on such attenuated
    regularities cannot, in general, be found by brute force
    search. The class of problems which present such mappings we
    call the class of `type-2 problems'. Type-1 problems, by
    contrast, present tractable problems of search insofar as the
    relevant regularities can be found by sampling the input data
    as originally coded.
        Type-2 problems, we suggest, present neither rare nor
    pathological cases. They are rife in biologically realistic
    settings and in domains ranging from simple animat behaviors to
    language acquisition. Not only are such problems rife - they
    are standardly solved! This presents a puzzle. How, given the
    statistical intractability of these type-2 cases does nature
    turn the trick?
        One answer, which we do not pursue, is to suppose that
    evolution gifts us with exactly the right set of re-coding
    biases so as to reduce specific type-2 problems to (tractable)
    type-1 mappings.  Such a heavy duty nativism is no doubt
    sometimes plausible.  But we believe there are other, more
    general mechanisms also at work.  Such mechanisms provide
    general (not task-specific) strategies for managing problems of
    type-2 complexity.
        Several such mechanisms are investigated. At the heart of each
    is a fundamental ploy viz. the maximal exploitation of states
    of representation already achieved by prior (type-1) learning
    so as to reduce the amount of subsequent computational search.
    Such exploitation both characterises and helps make unitary
    sense of a diverse range of mechanisms. These include simple
    incremental learning (Elman 1993), modular connectionism
    (Jacobs, Jordan and Barto 1991), and the developmental
    hypothesis of `representational redescription' (Karmiloff-Smith
    A Functional 1979, Karmiloff-Smith PDP 1992). In addition, the
    most distinctive features of human cognition---language and
    culture---may themselves be viewed as adaptations enabling this
    representation/computation trade-off to be pursued on an even
    grander scale.

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To help you decide whether you would be an appropriate commentator for
this article, an electronic draft is retrievable by anonymous ftp from
ftp.princeton.edu according to the instructions below (the filename is
bbs.clark). Please do not prepare a commentary on this draft.
Just let us know, after having inspected it, what relevant expertise
you feel you would bring to bear on what aspect of the article.
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These files are also on the World Wide Web and the easiest way to
retrieve them is with Netscape, Mosaic, gopher, archie, veronica, etc.
Here are some of the URLs you can use to get to the BBS Archive:

    http://www.princeton.edu/~harnad/bbs.html
    http://cogsci.soton.ac.uk/~harnad/bbs.html
    gopher://gopher.princeton.edu:70/11/.libraries/.pujournals
    ftp://ftp.princeton.edu/pub/harnad/BBS/bbs.clark
    ftp://cogsci.soton.ac.uk/pub/harnad/BBS/bbs.clark

To retrieve a file by ftp from an Internet site, type either:
ftp ftp.princeton.edu
   or
ftp 128.112.128.1
   When you are asked for your login, type:
anonymous
   Enter password as queried (your password is your actual userid:
   yourlogin at yourhost.whatever.whatever - be sure to include the "@")
cd /pub/harnad/BBS
   To show the available files, type:
ls
   Next, retrieve the file you want with (for example):
get bbs.clark
   When you have the file(s) you want, type:
quit

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