MétaCan
Menu
Back to cohort
Record W2111493386 · doi:10.1080/15250000902994073

Can Infants Use a Nonhuman Agent's Gaze Direction to Establish Word–Object Relations?

2009· article· en· W2111493386 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 · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Animal Learning Development
Canadian institutionsConcordia University
FundersConcordia UniversitySociety for Research in Child Development
KeywordsReferentGazePsychologyWord (group theory)Object (grammar)Focus (optics)Cognitive psychologyCommunicationNonverbal communicationLinguisticsArtificial intelligenceComputer science

Abstract

fetched live from OpenAlex

Adopting a procedure developed with human speakers, we examined infants' ability to follow a nonhuman agent's gaze direction and subsequently to use its gaze to learn new words. When a programmable robot acted as the speaker (Experiment 1), infants followed its gaze toward the word referent whether or not it coincided with their own focus of attention, but failed to learn a new word. When the speaker was human, infants correctly mapped the words (Experiment 2). Furthermore, when the robot interacted contingently, this did not facilitate infants' word mapping (Experiment 3). These findings suggest that gaze following upon hearing a novel word is not sufficient to learn the referent of the word when the speaker is nonhuman.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.467
Threshold uncertainty score1.000

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)0.0010.001

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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