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Can ACT-R Realize "Newell's Dream"?

2005· article· en· W2767274871 sur OpenAlexaboutno aff
Francisco Javier Sáinz Sánchez

Notice bibliographique

RevueeScholarship (California Digital Library) · 2005
Typearticle
Langueen
DomaineComputer Science
ThématiqueCognitive Science and Mapping
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSituatedCognitive scienceCognitive architectureCognitionContext (archaeology)Situated cognitionDreamPerceptionRepresentation (politics)EpistemologyMental representationPsychologyComputer scienceArtificial intelligencePhilosophyLawHistoryPolitical science
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Can ACT—R Realize “Newell’s Dream”? Matthew F. Rutledge-Taylor (mrtaylo2@connect.carleton.ca) Institute of Cognitive Science, Carleton University, 1125 Colonel By Drive Ottawa, Ontario, KIS 5B6 Canada Abstract In “The Atomic Components of Thought”, John Anderson and Christian Lebiere claim that ACT—R (4.0) realizes “Newell’s Dream” of a unifying theory of cognition. In this paper it is suggested that each ACT—R model can account for only a finite set of cognitive processes, and cannot therefore be used to model an unbounded whole mind. It is suggested that this is due to an inherent context dependence of ACT—R models. This limitation runs counter to the intuitive criterion that a unifying theory of cognition ought to be able to provide an account of the mind as a system not bound to any particular context. It is suggested that thought in cognitive models ought to be conceived as temporary context specific operations based on persistent context independent knowledge. The basis for a new cognitive architecture, which differentiates thought from knowledge is proposed. This new architecture combines ACT—R with elements of Lawrence Barsalou‘s situated simulation theory. Keywords: ACT—R; cognitive architectures; knowledge representation; situated cognition; perceptual symbols systems. Introduction In 1972, at the Carnegie Symposium on Cognition, Allan Newell raised a concern about the course of research in psychology. He delivered a paper entitled “You can’t play 20 questions with nature and win”, in which he lamented the fact that there was very little that unified the wealth of knowledge that had been accumulated about individual human cognitive processes (l973a). In a paper published separately in the proceedings of the symposium, Newell suggested that production systems might serve as detailed models of the human control structure (1973b; 1990). Eighteen years later, Newell published a book entitled “Unified Theories of Cognition” in which he proposed that cognitive architectures hold the key to unifying psychology (1990). There are many different cognitive architectures used to produce cognitive models of psychological phenomena, including Newell’s own SOAR architecture, which was first released in 1982 (Laird & Rosenbloom, 1996). However, the most popular architecture is ACT—R (Anderson, 1993; Anderson & Lebiere, 1998). This popularity is by no means accidental. The theory of cognition it implements has been well developed, and hence, has allowed a wide variety of researchers to produce theoretically grounded models of various cognitive phenomena. Additionally, and perhaps most significantly, ACT—R models typically fit the human experimental data they are designed to model, quite well. 1895 This paper considers whether ACT—R, as it currently exists, realizes “Newell’s Dream” of a unifying theory of cognition, or not. It is suggested that given appropriately strict criteria ACT—R may be inadequate. N ewell’s Criteria According to Newell: a theory is an explicit body of knowledge, from which answers to questions of a predictive, explanatory, or prescriptive type can be given; theories are approximate; theories cumulate; and, theories develop iteratively (1990, pp. 13-14). Newell defines a unified theory of cognition as “a single set of mechanisms for all of cognitive behavior” (1990, p. 15). He specifies these mechanisms as a prioritized list of areas of cognitive phenomena to be covered. They are, in order: problem solving, decision making, and routine action; memory, learning, and skill; perception, and motor behaviour; language; motivation, and emotion; and, imagining, dreaming, and, daydreaming. Thus, a complete unified theory of cognition, should account for all of these cognitive phenomena. However, given Newell’s views on theory development, an acceptable strategy would be to begin with a unified theory of the phenomena at the top of the list, and slowly augment the theory so as to accommodate successive items. Background Theory Ubiquitous in cognitive science is the View that cognitive systems can be analysed from a variety of perspectives. The tri-level hypothesis is that there are three basic levels of analysis (Dawson, 1998). Various researchers apply their own labels to these three levels. Newell divided them into the biological, cognitive, and rational (Newell, 1990); Zenon Pylyshyn makes use of the physical, syntactic, and semantic (Pylyshyn, 1999); and, Michael Dawson, the implementational, algorithmic, and computational levels (Dawson, 1998). Despite the difference in terms, there is arguably an equivalence between these hierarchies. The biological, physical, and implementational levels are, in the case of humans, the levels of description that (typically) appeal primarily to neural processes. The cognitive, syntactic, and algorithmic levels, describe human cognition in terms of operations on syntactic (or, otherwise, formal) structures. The rational, semantic, and computational levels are those at which the cognitive system is described in terms of its knowledge (i.e., goals, beliefs, and perceptions etc.). Pylyshyn asserts that a fundamental hypothesis in cognitive science is that this knowledge level is an autonomous (or, at least, partially autonomous) level of

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,006
score de la tête « metaresearch » (Gemma)0,012
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,029

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0060,012
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0020,017
Communication savante0,0080,019
Science ouverte0,0010,004
Intégrité de la recherche0,0030,004
Charge utile insuffisante (le modèle a refusé de juger)0,0080,003

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,015
Tête enseignante GPT0,219
Écart entre enseignants0,204 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2005
Routes d'admission1
Résumé présentoui

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