Free-viewing laterality tasks: A multilevel meta-analysis.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Chimeric free-viewing laterality tasks have been used extensively as measures of right-hemisphere functioning, with many variations in stimuli and samples typically showing an LVF bias. However, the questions remain concerning whether the LVF bias is significantly different from zero, and what factors might moderate this bias. METHOD: The present meta-analysis answered these questions by retrieving a presumably exhaustive sample of studies published in English that involved free viewing of stimuli. The final analysis was based on 329 effect sizes drawn from 112 published studies. A hierarchical linear model (or multilevel) approach to meta-analysis was used to deal with the violation of the independence of effect-sizes assumption and to reflect better the hierarchical structure of the data. RESULTS: A large and significant left visual-field (LVF) bias (estimated mean d = 1.024) was demonstrated across the entire set of retrieved effect sizes. It was also demonstrated that such tasks are a useful tool for discriminating between various clinical populations. Finally, the moderator analysis identified that emotion faces (estimated mean d = 1.052) and timed conditions (estimated mean d = 1.319) appear to promote large effects. CONCLUSIONS: The present meta-analysis validated free-viewing laterality tasks as tools for neuropsychological assessment and for empirical research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 it