Development and Validation of a Virtual Moving Auditory Localization (vMAL) Test among Healthy Children
Bibliographic record
Abstract
Introduction: The ability to localize sound sources is crucial for humans. Due to specific hearing disorders, the affected individuals may have problems to accurately locate the sound sources, leading to other unwanted consequences. Nevertheless, a simple auditory localization test (that employs moving auditory stimuli) is currently lacking in clinical settings. Essentially, the objectives of the present study were to develop a virtual moving auditory localization (vMAL) test that is suitable for assessing children and assess the validity and the reliability of this test. Materials and Methods: This study consisted of two consecutive phases. In phase 1, the required stimulus and the test set up for the vMAL test were established. Two loudspeakers were employed to produce five virtual positions, and eight different moving conditions were constructed. In phase 2, 24 normal-hearing Malaysian children (aged 7-12 years) underwent the vMAL test. The validity and the reliability of this test were then assessed using several validation measures. Fleiss Kappa and Spearman correlation analyses were used to analyse the obtained data. Results: The vMAL test was found to have good convergent validity (kappa = 0.64) and good divergent validity (kappa = -0.06). Based on the item-total correlation and Spearman coefficient rho results, this test was found to have good internal reliability (rho = 0.36-0.75) and excellent external (test-retest) reliability (rho = 0.99). Conclusions: in this study a new vMAL test was developed and proven to be valid and reliable accordingly for its intended applications. This test can be useful in clinical settings since it is simple to administer, cost-effective, does not take up much room, and can assess auditory localization performance in children. The outcomes of the present study may serve as preliminary normative data as well as guidelines for future auditory localization research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".