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Enregistrement W2587594965

The Influence of Outcome Severity on Ascriptions of Intention & Punishment

2007· article· en· W2587594965 sur OpenAlexaffabout
Aryn Pyke, Deepthi Kamawar, Diana Ridgeway

Notice bibliographique

RevueProceedings of the Annual Meeting of the Cognitive Science Society · 2007
Typearticle
Langueen
DomainePsychology
ThématiqueChild and Animal Learning Development
Établissements canadiensCarleton University
Organismes subventionnairesnon disponible
Mots-clésBlamePsychologyPunishment (psychology)Outcome (game theory)Social psychologyValence (chemistry)Action (physics)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The Influence of Outcome Severity on Ascriptions of Intention & Punishment Aryn Pyke (apyke@connect.carleton.ca) Deepthi Kamawar (dkamawar@ccs.carleton.ca) Diana Ridgeway (dianar@davidridgeway.com) Institute of Cognitive Science, Carleton University, 1125 Colonel By Drive Ottawa, ON, K1S 5B6 Canada Keywords: intention; TOM; folk psychology; moral judgment Introduction Are adults' and childrens' ascriptions of intention and allocations of punishment affected not only by the valence of a protagonist's intention (positive/negative), but also by the undesirability of (potentially unintended) outcome(s) of the protagonist’s action (e.g., something gets broken)? Even young children are sensitive to whether an outcome was intended when allocating blame/punishment (Nunez & Harris, 1998). However, the moral acceptability of the (possibly unintended) outcome of an action may also ‘retroactively’ influence the degree to which the outcome is judged intentional and thus blame/praiseworthy. In particular, negative unintended outcomes are more likely to be classified as intentional than positive unintended outcomes by children and adults (Knobe, 2003; Leslie, Knobe, & Cohen, 2006). In the present study, we focus on the negative side of the outcome spectrum (neutral, mildly negative, moderately negative), and we varied whether or not the protagonist’s motive had been to achieve a negative or positive outcome. We discuss how intention valence and outcome severity influenced ascriptions of intention and punishment, and moral ratings of the protagonist. Experiment Method. Forty undergraduates were divided into 2 groups (N A =18, N B =22). Each read 6 stories which featured either a well-intentioned protagonist (group A) or a negatively-intentioned protagonist (group B). For each story there were 3 possible outcomes. For example, in the positive-intention version of one story, Sally wants to share her cookies but accidentally drops them, resulting in: none breaking (neutral outcome); 1 breaking (mildly negative outcome); or 8 breaking (moderately negative outcome). In the negative-intentioned version of the story, Sally deliberately throws the cookies on the floor to break them and avoid sharing -- again the same three possible outcomes apply (neutral, mildly negative, and moderately negative). The outcomes varied across stories with each participant receiving two per type. Participants then ascribed intention ('Did she mean to drop the cookies?'), gave a moral rating for the protagonist (5-point scale), and assigned punishment (0=no punishment, 1=a little trouble, 2=a lot of trouble). Results. 2 (intention: positive, negative) x 3 (outcome: neutral, mildly negative, moderately negative) ANOVAs were conducted for each dependent variable: ascription of intention, moral rating, and assigned punishment. B Ascription of Intention. Participants recognized that positively-intentioned protagonists didn’t “mean to” cause the (negative) outcome, whereas negative-intentioned protagonists did, F(1,38)=342.4, p=.000. The severity of the outcome did not influence whether the outcome was classified as intentional, F(1.65,62.52)=1.9, p=.161. Moral Rating. The protagonist’s intention (pos, neg) influenced participants’ moral ratings for the protagonist, F(1,38)=126.6, p=.000. For each outcome, moral ratings were higher when the protagonist’s intention was positive than negative. However, negativity of the outcome also influenced moral ratings for the protagonist, F(1.54,76)=14.6, p=.000. Even for positive-intentioned protagonists, neutral outcomes prompted higher moral ratings than mild (p=.052) and moderate (p=.000) outcomes, and mild outcomes prompted higher ratings than moderate outcomes (p=.022). There was no interaction between intention and outcome severity, F(2,76) = 1.9, p = .153 Punishment. Degree of punishment was influenced both by intention valence and outcome severity, F(2,76)=15.8, p=.000, however there was no interaction. For both positive- and negative-intentioned protagonists, less punishment was assigned when the outcome was neutral versus mildly or moderately negative (ps<=.001), but there was no difference in assigned punishment between the latter two (p =.561). Discussion. Participants correctly discriminated whether the protagonists brought about the outcome by accident or on purpose, but moral ratings for the protagonist were nonetheless coloured by the severity of outcome. Further, a negative intention was sufficient to warrant some assignment of punishment, even if the action did not succeed in producing a negative outcome (no breakage). A positive intention was not sufficient to avoid allocation of punishment. More punishment was allocated when the outcome involved some destruction of property than when it did not, however degree of punishment did not vary further according to whether the degree of destruction was mild or moderate (whether some vs. all of the cookies broke). References Knobe, J. (2003). Intentional action in folk psychology. Philosophical Psychology, 16, 309-324. Leslie, A., Knobe, J., & Cohen, A. (2006). Acting intentionally and the side-effect effect. Psychological Science, 17, 421-427. Nunez, M. & Harris, P. L. (1998). Psychological and deontic concepts. Mind & Language, 13, 153-170.

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)0,005
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
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 enseignants0,154
Score d'incertitude au seuil0,892

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0050,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0010,002
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
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 Opus0,020
Tête enseignante GPT0,313
Écart entre enseignants0,294 · 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.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2007
Routes d'admission2
Résumé présentoui

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Même revueProceedings of the Annual Meeting of the Cognitive Science SocietyMême sujetChild and Animal Learning DevelopmentTravaux en français237 207