MétaCan
Menu
Back to cohort
Record W4404271611 · doi:10.1037/dev0001849

Remote Infant Studies of Early Learning (RISE): Scalable online replications of key findings in infant cognitive development.

2024· article· en· W4404271611 on OpenAlexaff
Elena J. Tenenbaum, Caitlin Stone, Michelle Vu, Kristen R Gilyard, Sudha Arunachalam, Elika Bergelson, Somer Bishop, Michael C. Frank, J. Kiley Hamlin, Melissa Kline Struhl, Rebecca Landa, Casey Lew‐Williams, Melissa E. Libertus, Rhiannon Luyster, Julie Markant, Maura Sabatos‐DeVito, Stephen J. Sheinkopf, J. Wagner, Anna I Soderling, Jordan Grapel, Amit Haim Bermano, Yotam Erel, Shafali Jeste

Bibliographic record

VenueDevelopmental Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of British Columbia
FundersNational Institute on Deafness and Other Communication DisordersSimons Foundation Autism Research Initiative
KeywordsPsychologyCognitive developmentKey (lock)CognitionDevelopmental psychologyChild developmentInfant developmentComputer science

Abstract

fetched live from OpenAlex

= 26; 14 female, 12 male; 62% White, 13% Asian, 1% Black, 1% Pacific Islander, 22% more than one race; 6% Hispanic). Infant looking behavior was recorded during at-home administration of the battery on the family's home computer and automatically coded for attention to stimuli using iCatcher+, an open-access software that assesses infant gaze direction. Results indicate that while some tasks replicated lab-based findings (attention, memory, prediction, and numeracy), others did not (word recognition, multimodal processing, and social evaluation). These findings will inform efforts to refine the battery as we continue to develop a robust set of tasks to improve the understanding of early cognitive development at the individual level in general and clinical populations. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.350
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.048
GPT teacher head0.392
Teacher spread0.344 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueDevelopmental PsychologySame topicLanguage Development and DisordersFrench-language works237,207