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Record W3150141661 · doi:10.5334/gjgl.1210

How children attend to events before speaking: crosslinguistic evidence from the motion domain

2021· article· en· W3150141661 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.

Bibliographic record

VenueGlossa a journal of general linguistics · 2021
Typearticle
Languageen
FieldPsychology
TopicCategorization, perception, and language
Canadian institutionsUniversity of Calgary
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Child Health and Human Development
KeywordsMotion (physics)PsychologyEvent (particle physics)LinguisticsTask (project management)Language productionEncoding (memory)Cognitive psychologyComputer scienceCognitionArtificial intelligence

Abstract

fetched live from OpenAlex

How do children talk about the dynamic world around them? In this eyetracking study, we demonstrate language-specific patterns in the way 3- and 4-year-old speakers of English and Greek inspect motion events prior to speaking and describe such events in their native language. Across age and language groups, children were more likely to mention manners of motion than paths, but English-speaking children were more likely to provide manner information than Greek-speaking children were. Comparison of eyegaze patterns from the linguistic (description) task to eyegaze patterns observed during a nonlinguistic (memory) task with a different group of English- and Greek-speaking 3- and 4-year-olds revealed effects of language background on event inspection. These effects suggest that by the age of 3 years, children exhibit sensitivities to language-specific patterns of motion event encoding that influence the way they gather information from the visual world during the process of language production.

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)0.006
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 score0.100
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
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)0.0010.000

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 head0.024
GPT teacher head0.315
Teacher spread0.291 · 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