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Record W1976201425 · doi:10.1080/15250000802329503

How to Build an Intentional Android: Infants' Imitation of a Robot's Goal‐Directed Actions

2008· article· en· W1976201425 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInfancy · 2008
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsUniversity of Toronto
FundersJapan Society for the Promotion of ScienceNatural Sciences and Engineering Research Council of CanadaNissan Global Foundation
KeywordsGazeImitationPsychologyRobotHumanoid robotEye contactHuman–robot interactionHuman–computer interactionObject (grammar)Action (physics)Eye trackingCognitive psychologyCommunicationDevelopmental psychologyArtificial intelligenceComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

This study examined whether young children are able to imitate a robot's goal‐directed actions. Children (24–35 months old) viewed videos showing a robot attempting to manipulate an object (e.g., putting beads inside a cup) but failing to achieve its goal (e.g., beads fell outside the cup). In 1 video, the robot made eye contact with a human before and after it failed the action. In another video, the robot did not make eye contact with the human adult. Only in the former condition did children “imitate” the robot's “intended” but unconsummated actions (e.g., putting beads inside a cup). When the robot did not make eye contact, children performed poorly, at the baseline level. These results suggest that human‐like gaze behaviors, not human‐like morphology, may play an important role in young children's imitation of a nonhuman agent's goal‐directed behaviors.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.328
Threshold uncertainty score0.501

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.033
GPT teacher head0.312
Teacher spread0.279 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it