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Enregistrement W2117504262 · doi:10.4324/9780203879313-24

Problems of representation II: naturalizing content

2009· book-chapter· en· W2117504262 sur OpenAlexaff
D.M. Ryder

Notice bibliographique

Revuenon disponible
Typebook-chapter
Langueen
DomainePsychology
ThématiquePhilosophy and Theoretical Science
Établissements canadiensUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésContent (measure theory)Representation (politics)Computer scienceMathematicsPolitical scienceLawMathematical analysis

Résumé

récupéré en direct d'OpenAlex

John is currently thinking that the sun is bright. Consider his occurrent belief or judgement that the sun is bright. Its content is that the sun is bright. This is a truthevaluable content (which shall be our main concern) because it is capable of being true or false.1 In virtue of what natural, scientically accessible facts does John’s judgement have this content? To give the correct answer to that question, and to explain why John’s judgement and other contentful mental states have the contents they do in virtue of such facts, would be to naturalize mental content. A related project is to specify, in a naturalistically acceptable manner, exactly what contents are. Truth-evaluable contents are typically identied with abstract objects called “propositions,” e.g. the proposition that the sun is bright. According to one standard story, this proposition is constituted by further abstract objects called “concepts”: a concept that denotes the sun and a concept that denotes brightness. These concepts are “combined” to form the proposition that the sun is bright. This proposition is the content of John’s belief, of John’s hope when he hopes that the sun is bright, of the sentence “The sun is bright,” of the sentence, “Le soleil est brillant,” and possibly one of the contents of John’s perception that the sun is bright, or of a painting that depicts the sun’s brightness.2 This illustrates the primary theoretical role of propositions (and concepts). Saying of various mental states and/or representations that they express a particular proposition P is to pick out a very important feature that they have in common. But what exactly is this feature? What are propositions and concepts, naturalistically speaking? Having raised this important issue, I will now push it into the background, and focus on the question of how mental statescan have contents, rather than on what contents are, metaphysically speaking. (That said, the most thoroughly naturalistic theories of content will include an account of propositions and concepts – compare the thoroughly naturalistic Millikan [1984], for instance, with McGinn [1989].) Whatever the ultimate nature of contents, the standard view among naturalists is that content is at least partly constituted by truth conditions (following e.g. Davidson [1967] and Lewis [1970] on the constitution of linguistic meaning). This review, then, will focus on naturalistic accounts of how mental states’ truth conditions are determined. That said, “content” is clearly a philosophical term of art, so there is a large degree of exibility as to what aspects of a mental state count as its content, and therefore what a theory of content ought to explain. For example, is it possible for me, you, a blind person, a robot, a chimpanzee, and a dog to share the belief “that the stop sign is red,” concerning a particular stop sign? Clearly, there are differences among the mental states that might be candidates for being such a belief, but it is not immediately obvious which of those differences, if any, are differences in content. It seems that contents pertain both to certain mental states (like John’s judgement) and to representations (like a sentence).3 It would simplify matters a lot if contentful mental states turned out to be representations also. This is a plausible hypothesis (see Chapters 7, 10, 17, and 23 of this volume), and almost universally adopted by naturalistic theories of content. On this hypothesis, the content of a particular propositional attitude is inherited from the content of the truth-evaluable mental representation that features in it. What we are in search of, then, is a naturalistic theory of content (including, at least, truth conditions) for these mental representations, or in Fodor’s (1987) terms, a “psychosemantic theory,” analogous to a semantic theory for a language. Soon we will embark on a survey of such theories, but rst, a couple of relatively uncontroversial attributive (ATT) desiderata.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Autre · Signal consensuel: Autre
Score de désaccord entre enseignants0,353
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

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 Opus0,121
Tête enseignante GPT0,331
Écart entre enseignants0,210 · 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 tête enseignante, pas un consensus.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreAutre

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
Publié2009
Routes d'admission1
Résumé présentoui

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