Keeping an Eye on Looking Measures: Towards More Robust Developmental Methods
Bibliographic record
Abstract
A persistent challenge in experimental developmental psychology is determining which of many possible outcome measures best captures underlying behaviors and processes. In the looking-while-listening paradigm for studying early word comprehension, researchers have developed more than 12 distinct outcome measures, but have limited empirical basis for choosing between them. Using archival data from 15 datasets (N = 602 children, 12-60 months), we evaluated these measures' psychometric properties. We found that: (1) proportion looking, reaction time, and proportion of trials switching from distractor to target, demonstrated the strongest validity, robustness (experimental effect size), and reliability; and (2) the paradigm captures two distinct cognitive processes-detecting mismatches and confirming matches-with distractor-initial trials showing stronger developmental sensitivity. This work provides both specific recommendations for word comprehension research and a reproducible framework for evaluating measurement approaches in experimental developmental science. SUMMARY: A challenge in experimental developmental psychology is the proliferation of outcome measures for the same construct, without psychometric information to adjudicate between measures. We investigated the validity, robustness (effect size), and reliability of 12 distinct outcome measures for the looking-while-listening task, across 15 datasets collected from 602 infants. Proportion looking, reaction time, and proportion of trials switching from distractor to target were the most psychometrically sound outcome measures. Distractor-initial trials showed greater developmental sensitivity than target-initial trials. Through these two trial types, looking-while-listening captures two distinct cognitive processes: detecting mismatches and confirming matches.
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 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 | 0.346 | 0.625 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".