Bilingualism Affects Infant Cognition: Insights From New and Open Data
Bibliographic record
Abstract
Abstract Bilingualism has been hypothesized to shape cognitive abilities across the lifespan. Here, we examined the replicability of a seminal study that showed monolingual–bilingual differences in infancy (Kovács & Mehler, 2009a) by collecting new data from 7-month-olds and 20-month-olds and reanalyzing three open datasets from 7- to 9-month-olds (D’Souza et al., 2020; Kalashnikova et al., 2020, 2021). Infants from all studies (N = 222) were tested in an anticipatory eye-tracking paradigm, where they learned to use a cue to anticipate a reward presented on one side of a screen during Training, and the opposite side at Test. To correctly anticipate the reward at Test, infants had to update their previously learned behavior. Across four out of five studies, a fine-grained analysis of infants’ anticipations showed that bilinguals were better able to update the previously learned response at Test, which could be related to bilinguals’ weaker initial learning during Training. However, in one study of 7-month-olds, we observed the opposite pattern: bilinguals performed better during Training, and monolinguals performed better at Test. These results show that bilingualism affects how infants process information during learning. We also highlight the potential of open science to advance our understanding of language development.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".