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Enregistrement W2074644642 · doi:10.1037/h0085797

Informal reasoning: Theory and method.

2004· article· en· W2074644642 sur OpenAlexaff
Jonathan St. B. T. Evans, Valerie A. Thompson

Notice bibliographique

RevueCanadian Journal of Experimental Psychology/Revue canadienne de psychologie expérimentale · 2004
Typearticle
Langueen
DomaineDecision Sciences
ThématiqueDecision-Making and Behavioral Economics
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésPsychologyCognitive psychologyCognitive science

Résumé

récupéré en direct d'OpenAlex

The rationale for the present volume is simple: The great majority the everyday reasoning, including that expert groups engaged their professions, is informal. By contrast, most the studies human inference reported by psychologists the literature are formal reasoning. This discrepancy provides considerable cause for concern and not only because cognitive psychology should have some practical application. Excessive focus on formal reasoning tasks has also, our view, inhibited the development good theories human reasoning. What Is Informal Reasoning, and Why Do We Need to Study It? Psychological studies formal reasoning have fallen largely into two domains: deductive reasoning and statistical inference. These two endeavours have much common and some researchers work both areas. In both cases, participants are presented with what problem-solving researchers call well-defined problems. A well-defined problem can be solved by use the information provided and no other; fact, the correct solution to these problems often requires the reasoner to use only the information provided the premises, and to avoid adding background information and knowledge to the problem domain. Instead, a correct solution is achieved by applying a normatively appropriate rule inference. Normative systems are often applied to formal reasoning problems order to define solutions as right or wrong, such that these problems are then construed as tests correct and fallacious reasoning. Hence, these problems are designed to measure the extent to which participants bring to the laboratory an understanding and ability to apply - the relative normative principles. In the case deductive reasoning research, the relevant normative system is formal logic. Participants are given some premises and asked whether a conclusion follows. Under strict deductive reasoning instructions, they are told (a) to assume that the premises are true and (b) to draw or approve only conclusions that necessarily follow. As observed elsewhere (Evans, 2002), this widely used method was developed over 40 years ago when belief logic as a normative and descriptive system for human reasoning was veiy much higher than it is today. In spite the method, much evidence has emerged to support the conclusion that pragmatic factors play a large part human reasoning. We say in spite of because standard deductive instructions aim to suppress precisely those factors that dominate informal reasoning: the introduction prior belief and the expression uncertainty premises and conclusions. In research on statistical inference, a similar story is found. People are asked to make statistical inference on the basis well-defined problems, which relevant probabilities or frequency distributions are provided, and their answers are assessed for correctness against the norms provided by the probability calculus. Research this tradition has been mostly conducted by researchers the heuristics and biases tradition inspired by the work Danny Kahneman and Amos Tversky (Gilovich, Griffin, & Kahneman, 2002; Kahneman, Slovic, & Tversky, 1982). This results an arguably negative research strategy that is similar to much work on deductive reasoning. That is, researchers show primarily what people cannot do (conform to the principles logic or probability theory) and only secondarily address what people actually do. Indeed, one the most common explanations for why intelligent, educated individuals often fail to reason normatively is that they use informal reasoning processes to solve formal reasoning tasks. For example, notwithstanding instructions to the contraiy, reasoners often supplement the information they are provided with background knowledge and beliefs, and make inferences that are consistent with, rather than necessitated by, the premises. If this is the case, it is reasonable to suggest that we study these processes directly, by giving our participants tasks that allow them to express these types behaviours freely, rather than indirectly, via the observation poor performance on a formal task. …

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)0,006
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,553
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0060,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0020,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,101
Tête enseignante GPT0,425
Écart entre enseignants0,324 · 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
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

Citations30
Publié2004
Routes d'admission1
Résumé présentoui

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