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Children's Causal Learning from Fiction: Assessing the Proximity Between Real and Fictional Worlds - eScholarship

2012· article· en· W2765156980 sur OpenAlexaboutno aff
Caren M. Walker, Patricia A. Ganea, Alison Gopnik

Notice bibliographique

RevueProceedings of the Annual Meeting of the Cognitive Science Society · 2012
Typearticle
Langueen
DomainePsychology
ThématiqueEducational Strategies and Epistemologies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésPremiseFantasyContext (archaeology)PsychologyFictional universeEpistemologyCognitive scienceArtPhilosophyLiteratureHistoryNarrative
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Children’s Causal Learning from Fiction: Assessing the Proximity Between Real and Fictional Worlds Caren M. Walker 1 , Patricia A. Ganea 2 , & Alison Gopnik 1 (caren.walker@berkeley.edu, patricia.ganea@utoronto.ca, gopnik@berkeley.edu) Psychology Department, University of California, Berkeley, 3210 Tolman Hall, Berkeley, CA 94720 Human Development & Applied Psychology, University of Toronto, 56 Spadina Rd., Toronto, Canada M5R 2TI Abstract this perceived similarity by presenting information in a fictional world that seamlessly interweaves fantasy and reality (Woolley & Cox, 2007). Even in explicitly pedagogical scenarios, teachers often embed their intended curriculum within a fantasy context. This decision is based on the assumption that fictional worlds are more engaging to the young child, and may therefore encourage increased sustained attention and learning of novel material (Harris, 2000; Renninger & Wozniak, 1985). Previous research supports the proposal that fantasy contexts serve to improve children’s performance on certain types of cognitive tasks, such as deductive and syllogistic reasoning and theory of mind (e.g., Dias & Harris, 1988; Dias, Roazzi, & Harris, 2005; Hawkins, Pea, Glick & Scribner, 1984; Richards & Sanderson, 1999; Lillard & Sobel, 1999; Sobel & Lillard, 2001). For example, according to Dias et al. (2005), placing an unfamiliar premise in a fantastical context – particularly when the premise directly contradicts a currently-held theory – allows children to override their natural empirical orientation, or bias to reason in line with their past experiences. It is unknown, however, how learning and generalization of novel causal information (which does not require the suspension of existing knowledge) is affected by the fantastical contexts of the fictional stories in which this information is embedded. The ability to effectively process fictional information is dependent upon a variety of representational skills, including at least two major factors that are unique to learning from fictional material. The first includes the development of a mature concept about the boundary between the fictional and real world, as well as an understanding of what information is more appropriately quarantined to the fictional space. Second, it is necessary for children to develop an understanding of when it is appropriate to transfer information from the fictional to the real world, and what contextual cues should be considered in this decision. The current research explores the early development of each of these factors, and in particular, examines whether children’s sensitivity to contextual cues in fictional worlds changes over the course of development. Fictional information presents a unique challenge to the developing child. Children must learn when it is appropriate to transfer information from the fictional space to the real world and what contextual cues should be considered in this decision. The current research explores children’s causal inferences between fictional representations and reality by examining their developing sensitivity to the proximity of the fictional world to the real world, and the effect of this judgment on their subsequent generalization of novel causal properties. By 3-years of age, children are able to evaluate the data that they receive from fictional stories in order to inform their generalization of novel story content to the real world. Additionally, as children develop, they become better able to discriminate between close (realistic) and far (fantastical) fictional worlds when assessing which stories are likely to provide relevant causal knowledge. Keywords: causal inference; fiction; cognitive development; prior knowledge; representation The ‘Reader’s Dilemma’ Children’s growing knowledge about the world comes from a variety of sources, including their exposure to fictional material. In fact, much of the unfamiliar information that children encounter appears in the context of stories and fantastical representations of the world. Children, like adults, therefore often encounter the “reader’s dilemma”: the need to compartmentalize fictional information to insulate real world knowledge from false facts, and the simultaneous need to incorporate this information due to its potential application to a host of real world topics (Gerrig & Prentice, 1999; Potts, St. John, & Kirson, 1989). There is substantial evidence in developmental psychology that indicates that the ability to distinguish reality from fiction develops significantly during the preschool years (e.g., DeLoache, Pierroutakos, Uttal, Rosengren, & Gottlieb, 1998; Flavell, Flavell, & Green, 1989; Woolley & Cox, 2007; Woolley & Wellman, 1990; Woolley & Van Reet, 2006). However, very little research has explored children’s ability to learn causal information about the real world from their exposure to fictional material. Fictional information presents a unique challenge to the developing child. Research has shown that the transfer of knowledge is generally facilitated by similarity between the context in which the information is learned, and the context in which it is to be applied (Catranbone & Holyoak, 1989; Spencer & Weisberg, 1986). However, many of the learning contexts that are created for young children act to reduce Children’s Beliefs about Fictional Worlds There is a growing literature in developmental psychology regarding when and how children distinguish between fantasy and reality. Methods for testing this distinction vary

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)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesÉtudes des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,064
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0020,002
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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 Opus0,040
Tête enseignante GPT0,336
Écart entre enseignants0,296 · 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'étudeObservationnel
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

Citations0
Publié2012
Routes d'admission1
Résumé présentoui

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Même revueProceedings of the Annual Meeting of the Cognitive Science SocietyMême sujetEducational Strategies and EpistemologiesTravaux en français237 207