Limited evidence of test-retest reliability in infant-directed speech preference in a large pre-registered infant experiment
Bibliographic record
Abstract
Test-retest reliability --- establishing that measurements remain consistent across multiple testing sessions --- is critical to measuring, understanding, and predicting individual differences in infant language development. However, previous attempts to establish measurement reliability in infant speech perception tasks are limited, and reliability of frequently-used infant measures is largely unknown. The current study investigated the test-retest reliability of infants' preference for infant-directed speech over adult-directed speech in a large sample (N=158) in the context of the ManyBabies1 collaborative research project (Frank et al., 2017; ManyBabies Consortium, 2020). Labs were asked to bring in participating infants for a second appointment retesting infants on their preference for infant-directed speech. This approach allowed us to estimate test-retest reliability across three different methods used to investigate preferential listening in infancy: the head-turn preference procedure, central fixation, and eye-tracking. Overall, we found no consistent evidence of test-retest reliability in measures of infants' speech preference (overall r=.09, 95% CI [-.06,.25]). While increasing the number of trials that infants needed to contribute for inclusion in the analysis revealed a numeric growth in test-retest reliability, it also considerably reduced the study's effective sample size. Therefore, future research on infant development should take into account that not all experimental measures may be appropriate for assessing individual differences between infants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".