Children's Understanding of Intentional Causation in Moral Reasoning About Harmful Behaviour
Notice bibliographique
Résumé
When evaluating a situation that results in harm, it is critical to consider how a personâs prior intention may have been causally responsible for the action that resulted in the harmful outcome. This thesis examined childrenâs developing understanding of intentional causation in reasoning about harmful outcomes, and the relation between this understanding and mental-state reasoning. \n\tFour-, 6-, and 8-year-old children, and adults, were told eight stories in which charactersâ actions resulted in harmful outcomes. Story types differed in how the actions that resulted in harm were causally linked to their prior intentions such that: (1) characters wanted to, intended to, and did perform a harmful act; (2) they wanted and intended to perform a harmful act, but instead, accidentally brought about the harmful outcome; (3) they wanted and intended to perform a harmful act, then changed their mind, but accidentally brought about the harmful outcome; (4) they did not want or intend to harm, but accidentally brought about a harmful outcome. Participants were asked to judge the charactersâ intentions, make punishment judgments, and justify their responses. Additionally, children were given first- and second-order false-belief tasks, commonly used to assess mental-state reasoning. \n\tThe results indicated that intention judgment accuracy improved with age. However, all age groups had difficulty evaluating the intention in the deviant causal chain scenario (Searle, 1983), in which the causal link between intention and action was broken but a harmful intention was maintained. Further, the results showed a developmental pattern in childrenâs punishment judgments based on their understanding of intentional causation, although the adultsâ performance did not follow the same pattern. Also, younger children referred to the charactersâ intentions less frequently in their justifications of their punishment judgments. \n\tThe results also revealed a relation between belief-state reasoning and intentional-causation reasoning in scenarios that did not involve, or no longer involved, an intention to harm. Further, reasoning about intentional causation was related to higher-level understanding of mental states. The implications of these findings in clarifying and adding to previous research on the development of understanding of intentional causation and intentions in moral reasoning are discussed.
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Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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