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Record W4226257561 · doi:10.1525/collabra.31977

What Can We Perceive in Infant Vocalization?

2022· article· en· W4226257561 on OpenAlexaff
Alanna Beyak, Olivia Cadieux, Matt Cook, Carly Cressman, Barbie Jain, Jarod A. Joshi, Spenser L. Martin, Michael Mielniczek, Sara Montazeri, Essence I. Perara, Jolyn Sawatzky, Bradley C. Smith, Jackie Spear, Thomas Thompson, Derek Trudel, Jianjie Zeng, Mélanie Söderström

Bibliographic record

VenueCollabra Psychology · 2022
Typearticle
Languageen
FieldHealth Professions
TopicInfant Health and Development
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCLIPSPsychologyPerceptionDevelopmental psychologyInfant developmentLanguage developmentMedicine

Abstract

fetched live from OpenAlex

Infant language development includes a complex social dynamic between adults and infants. Infant vocalization is a well-studied area of development, however adult perception of infant vocalization is less well-understood. The effectiveness of identifications made by adults may impact the social feedback loops that drive development. We collected data from a final sample of 460 undergraduate students who listened to brief (100-500 ms) audio clips of infant vocalization. Participants were asked to identify infants in the audio clips as male/female, English/non-English, and their approximate age. Participants were unable to determine the sex of the infant better than chance but showed better than chance performance for language and age, albeit with low accuracy. Exploratory follow-up analyses did not reveal an effect of caregiving experience, childcare experience, or participant gender on a participants’ ability to correctly identify the infant’s age, sex, or language. These findings suggest that adult caregivers, regardless of experience, are able to perceive elements of infant vocalizations that may influence responsiveness to infant vocal development. However, performance is far from perfect.

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.

How this classification was reachedexpand

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.327
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.057
GPT teacher head0.455
Teacher spread0.398 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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