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Record W2584776207 · doi:10.1111/desc.12537

Older but not younger infants associate own‐race faces with happy music and other‐race faces with sad music

2017· article· en· W2584776207 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

VenueDevelopmental Science · 2017
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsUniversity of Toronto
FundersNational Institutes of HealthNatural Science Foundation of Zhejiang ProvinceNatural Sciences and Engineering Research Council of CanadaNational Natural Science Foundation of China
KeywordsPsychologyRace (biology)Valence (chemistry)Association (psychology)MusicalFace (sociological concept)Developmental psychologyEmotional valenceCognitionLinguisticsVisual arts

Abstract

fetched live from OpenAlex

We used a novel intermodal association task to examine whether infants associate own- and other-race faces with music of different emotional valences. Three- to 9-month-olds saw a series of neutral own- or other-race faces paired with happy or sad musical excerpts. Three- to 6-month-olds did not show any specific association between face race and music. At 9 months, however, infants looked longer at own-race faces paired with happy music than at own-race faces paired with sad music. Nine-month-olds also looked longer at other-race faces paired with sad music than at other-race faces paired with happy music. These results indicate that infants with nearly exclusive own-race face experience develop associations between face race and music emotional valence in the first year of life. The potential implications of such associations for developing racial biases in early childhood are discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0010.000
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 head0.041
GPT teacher head0.277
Teacher spread0.236 · 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