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Trading spaces : Computation, representation, and the limits of uninformed learning. Commentary. Authors' response

Identifieur interne : 00CF16 ( Main/Exploration ); précédent : 00CF15; suivant : 00CF17

Trading spaces : Computation, representation, and the limits of uninformed learning. Commentary. Authors' response

Auteurs : A. Clark [États-Unis] ; C. Thornton [Royaume-Uni] ; I. S. N. Berkeley [États-Unis] ; J. A. Bullinaria [Royaume-Uni] ; N. Chater [Royaume-Uni] ; R. I. Damper [Royaume-Uni] ; T. Dartnall [Australie] ; P. F. Dominey [France] ; M. Elton [Royaume-Uni] ; M. G. Gaskell [Royaume-Uni] ; R. M. Golden [États-Unis] ; K. Haberlandt [États-Unis] ; G. S. Halford [Australie] ; K. Kurtz [États-Unis] ; D. Leiser [Israël] ; G. Marcus [États-Unis] ; D. Memmi [France] ; J. Oberlander [Royaume-Uni] ; S. Ohlsson [États-Unis] ; P. Skokowski [Royaume-Uni] ; R. S. Stufflebeam [États-Unis] ; N. Szilas [Canada] ; T. R. Shultz [Canada] ; A. Vinter [France] ; P. Perruchet [France] ; A. Wells [Royaume-Uni]

Source :

RBID : Pascal:97-0434999

Descripteurs français

English descriptors

Abstract

Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding ofthe data. The space of possible recodings is, however, infinitely large - it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that 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 (simulated animal or autonomous robot) 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 recoding 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 - namely, the maximal exploitation of states of representation already achieved by prior, simpler (type-1) learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of diverse range of mechanisms. These include simple incremental learning (Elman 1993), modular connectionism (Jacobs et al. 1991), and the developmental hypothesis of "representational redescription" (Karmiloff-Smith 1979; 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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<s1>Department of Behavioral Sciences, Ben-Gurion University of the Negev</s1>
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<country>Israël</country>
<wicri:noRegion>Beer Sheva</wicri:noRegion>
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<s1>Department of Psychology, University of Massachusetts</s1>
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<country>États-Unis</country>
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<orgName type="university">Université du Massachusetts</orgName>
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<name sortKey="Memmi, D" sort="Memmi, D" uniqKey="Memmi D" first="D." last="Memmi">D. Memmi</name>
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<s1>LEIBNIZ-IMAG-CNRS, 46 avenue Felix Viallet</s1>
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<country>France</country>
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<region type="region" nuts="2">Auvergne-Rhône-Alpes</region>
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<settlement type="city">Grenoble</settlement>
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<name sortKey="Oberlander, J" sort="Oberlander, J" uniqKey="Oberlander J" first="J." last="Oberlander">J. Oberlander</name>
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<s1>Human Communication Research Centre, University of Edinburgh</s1>
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<country>Royaume-Uni</country>
<placeName>
<settlement type="city">Édimbourg</settlement>
<region type="country">Écosse</region>
</placeName>
<orgName type="university">Université d'Édimbourg</orgName>
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<name sortKey="Ohlsson, S" sort="Ohlsson, S" uniqKey="Ohlsson S" first="S." last="Ohlsson">S. Ohlsson</name>
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<s1>Department of Psychology, University of Illinois at Chicago, (M/C 285)</s1>
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<country>États-Unis</country>
<wicri:noRegion>Chicago, IL 60607-7137</wicri:noRegion>
<orgName type="university">Université de l'Illinois à Chicago</orgName>
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<settlement type="city">Chicago</settlement>
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<s1>McDonnell-Pew Centre for Cognitive Neuroscience, Oxford University</s1>
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<country>Royaume-Uni</country>
<wicri:noRegion>Oxford, OX1 3UD</wicri:noRegion>
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<s1>Philosophy-Neuroscience-Psychology Program, Washington University, Campus Box 1073</s1>
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<country>États-Unis</country>
<wicri:noRegion>St. Louis, MO 63130-4899</wicri:noRegion>
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<name sortKey="Szilas, N" sort="Szilas, N" uniqKey="Szilas N" first="N." last="Szilas">N. Szilas</name>
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<s1>Department of Psychology, McGill University</s1>
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<sZ>22 aut.</sZ>
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<country>Canada</country>
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<settlement type="city">Montréal</settlement>
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<orgName type="university">Université McGill</orgName>
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<name sortKey="Shultz, T R" sort="Shultz, T R" uniqKey="Shultz T" first="T. R." last="Shultz">T. R. Shultz</name>
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<s1>Department of Psychology, McGill University</s1>
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<sZ>22 aut.</sZ>
<sZ>23 aut.</sZ>
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<settlement type="city">Montréal</settlement>
<region type="state">Québec</region>
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<orgName type="university">Université McGill</orgName>
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<name sortKey="Vinter, A" sort="Vinter, A" uniqKey="Vinter A" first="A." last="Vinter">A. Vinter</name>
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<s1>L.E.A.D., C.N.R.S., University of Bourgogne</s1>
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<country>France</country>
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<region type="region" nuts="2">Bourgogne-Franche-Comté</region>
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<author>
<name sortKey="Perruchet, P" sort="Perruchet, P" uniqKey="Perruchet P" first="P." last="Perruchet">P. Perruchet</name>
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<s1>L.E.A.D., C.N.R.S., University of Bourgogne</s1>
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<country>France</country>
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<region type="region" nuts="2">Bourgogne-Franche-Comté</region>
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<author>
<name sortKey="Wells, A" sort="Wells, A" uniqKey="Wells A" first="A." last="Wells">A. Wells</name>
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<s1>Department of Social Psychology, The London School of Economics and Political Science</s1>
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<series>
<title level="j" type="main">Behavioral and brain sciences</title>
<title level="j" type="abbreviated">Behav. brain sci.</title>
<idno type="ISSN">0140-525X</idno>
<imprint>
<date when="1997">1997</date>
</imprint>
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<title level="j" type="main">Behavioral and brain sciences</title>
<title level="j" type="abbreviated">Behav. brain sci.</title>
<idno type="ISSN">0140-525X</idno>
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<keywords scheme="KwdEn" xml:lang="en">
<term>Acquisition process</term>
<term>Cognition</term>
<term>Connectionism</term>
<term>Critical study</term>
<term>Human</term>
<term>Learning</term>
<term>Neural network</term>
<term>Representation</term>
<term>Statistical method</term>
</keywords>
<keywords scheme="Pascal" xml:lang="fr">
<term>Apprentissage</term>
<term>Connexionnisme</term>
<term>Réseau neuronal</term>
<term>Représentation</term>
<term>Méthode statistique</term>
<term>Etude critique</term>
<term>Cognition</term>
<term>Processus acquisition</term>
<term>Homme</term>
</keywords>
<keywords scheme="Wicri" type="topic" xml:lang="fr">
<term>Méthode statistique</term>
<term>Homme</term>
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<front>
<div type="abstract" xml:lang="en">Some regularities enjoy only an attenuated existence in a body of training data. These are regularities whose statistical visibility depends on some systematic recoding ofthe data. The space of possible recodings is, however, infinitely large - it is the space of applicable Turing machines. As a result, mappings that pivot on such attenuated regularities cannot, in general, be found by brute-force search. The class of problems that 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 (simulated animal or autonomous robot) 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 recoding 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 - namely, the maximal exploitation of states of representation already achieved by prior, simpler (type-1) learning so as to reduce the amount of subsequent computational search. Such exploitation both characterizes and helps make unitary sense of diverse range of mechanisms. These include simple incremental learning (Elman 1993), modular connectionism (Jacobs et al. 1991), and the developmental hypothesis of "representational redescription" (Karmiloff-Smith 1979; 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.</div>
</front>
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<li>Bourgogne</li>
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<li>Illinois</li>
<li>Massachusetts</li>
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