Associative word learning in infancy: A meta-analysis of the switch task.
Bibliographic record
Abstract
Associative word learning, the ability to pair a concept to a word, is an essential mechanism for early language development. One common method by which researchers measure this ability is the Switch task (Werker, Cohen, Lloyd, Casasola, & Stager, 1998), wherein infants are habituated to 2 word-object pairings and then tested on their ability to notice a switch in those pairings. In this comprehensive meta-analysis, we summarized 141 Switch task studies involving 2,723 infants of 12 to 20 months to estimate an average effect size for the task (random-effect model) and to explore how key experimental factors affect infants' performance (fixed-effect model). The average effect size was low to moderate in size, Cohen's d = 0.32. The use of language-typical and dissimilar-sounding words as well as the presence of additional facilitative cues aided performance, particularly for younger infants. Infants learning 2 languages at home outperformed those learning 1, indicating a bilingual advantage in learning word-object associations. Together, these findings support the Processing Rich Information from Multidimensional Interactive Representations (PRIMIR) theoretical framework of infant speech perception and word learning (e.g., Werker & Curtin, 2005), but invite further theoretical work to account for the observed bilingual advantage. Lastly, some of our analyses raised the possibility of questionable research practices in this literature. Therefore, we conclude with suggestions (e.g., preregistration, transparent data peeking, and alternate statistical approaches) for how to address this important issue. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.018 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.024 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".