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Linking Infant-Directed Speech and Face Preferences to Language Outcomes in Infants at Risk for Autism Spectrum Disorder

2013· article· en· W2166871817 on OpenAlexafffund
Danielle Droucker, Suzanne Curtin, Athena Vouloumanos

Bibliographic record

VenueJournal of Speech Language and Hearing Research · 2013
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsAutism spectrum disorderPsychologyLanguage developmentDevelopmental psychologyPreferenceAutismSpeech perceptionTypically developingLanguage delayAudiologyPerceptionMedicine

Abstract

fetched live from OpenAlex

PURPOSE: In this study, the authors aimed to examine whether biases for infant-directed (ID) speech and faces differ between infant siblings of children with autism spectrum disorder (ASD) (SIBS-A) and infant siblings of typically developing children (SIBS-TD), and whether speech and face biases predict language outcomes and risk group membership. METHOD: Thirty-six infants were tested at ages 6, 8, 12, and 18 months. Infants heard 2 ID and 2 adult-directed (AD) speech passages paired with either a checkerboard or a face. The authors assessed expressive language at 12 and 18 months and general functioning at 12 months using the Mullen Scales of Early Learning (Mullen, 1995). RESULTS: Both infant groups preferred ID to AD speech and preferred faces to checkerboards. SIBS-TD demonstrated higher expressive language at 18 months than did SIBS-A, a finding that correlated with preferences for ID speech at 12 months. Although both groups looked longer to face stimuli than to the checkerboard, the magnitude of the preference was smaller in SIBS-A and predicted expressive vocabulary at 18 months in this group. Infants' preference for faces contributed to risk-group membership in a logistic regression analysis. CONCLUSION: Infants at heightened risk of ASD differ from typically developing infants in their preferences for ID speech and faces, which may underlie deficits in later language development and social communication.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.179
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.047
GPT teacher head0.369
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations35
Published2013
Admission routes2
Has abstractyes

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