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People do not always know best: Preschoolers’ trust in social robots versus humans

2023· dissertation· en· W7061161103 sur OpenAlexfundno aff

Notice bibliographique

RevueSpectrum Research Repository (Concordia University) · 2023
Typedissertation
Langueen
DomainePhysics and Astronomy
ThématiqueGyrotron and Vacuum Electronics Research
Établissements canadiensnon disponible
Organismes subventionnairesSocial Sciences and Humanities Research Council of CanadaConcordia University
Mots-clésConceptualizationRobotSocial robotTest (biology)Humanoid robotHuman–robot interactionNonsenseRobotics
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

The main goal of my thesis was to investigate how 3- and 5-year-old children learn from robots versus humans using a selective trust paradigm. Children’s conceptualization of robots was also investigated. By using robots, which lack many of the social characteristics human informants possess by default, these studies sought to test young children’s reliance on epistemic characteristics conservatively. 
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\nIn Study 1, a competent humanoid robot, Nao, and an incompetent human, Ina, were presented to children. Both informants labelled familiar objects, like a ball, with Nao labelling them correctly and Ina labelling them incorrectly. Next, both informants labelled novel items with nonsense labels. Children were then asked what the novel item was called. Children were also asked what should go inside robots, something biological or something mechanical. Study 2 followed the same paradigm as Study 1, with the only change being the robot used, now the non-humanoid Cozmo. Eliminating the human-like appearance of the robot made for an even more conservative test than in Study 1. Both studies 1 and 2 found that 3-year-old children learned novel words equally from the robot and the human, regardless of the robot’s morphology. The 3-year-old children were also confused about both robot’s internal properties, attributing mechanical and biological insides to the robots equally. In contrast, the 5-year-olds in both studies preferred to learn from the accurate robot over the inaccurate human. The 5-year-olds also learned from both robots despite understanding that the robot is different from themselves; they attributed mechanical insides to both Nao and Cozmo over biological insides. 
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\nStudy 3 further investigated 3-year-olds ambivalence regarding their trust judgements, that is, who they choose to learn from. Instead of word learning, the robot demonstrated competence through pointing. The robot would accurately point at a toy inside a transparent box, and the human would point at an empty box. Next, both informants pointed at opaque boxes and the child was asked where the toy was located. Neither informant demonstrated the ability to speak, as speech is a salient social characteristic. 3-year-olds were still at chance, equally endorsing the robot and the human’s pointing. This suggests that goal-directedness and autonomous movement may be the most important characteristics used to signal agency for young children. The 3-year-olds were also still unsure about the robot’s biology, whereas they correctly identified the human as biological. This suggests that robots are confusing for children due to their dual nature as animate and yet not alive. 
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\nThis thesis shows that by the age of 5, children are willing and able to learn from a robot. These studies further add to the selective trust literature and have implications for educational settings.

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)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Études des sciences et des technologies, Intégrité de la recherche
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 enseignants0,384
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,000
Communication savante0,0000,000
Science ouverte0,0020,000
Intégrité de la recherche0,0010,004
Charge utile insuffisante (le modèle a refusé de juger)0,0010,001

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 Opus0,042
Tête enseignante GPT0,328
Écart entre enseignants0,286 · 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
Publié2023
Routes d'admission1
Résumé présentoui

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