Variability and reduced performance of preschool- and early school-aged children on psychoacoustic tasks: What are the relevant factors?
Bibliographic record
Abstract
Young children typically perform more poorly on psychoacoustic tasks than do adults, with large individual differences. When performance is averaged across children within age groups, the data suggest a gradual change in performance with increasing age. However, an examination of individual data suggests that the performance matures more rapidly, although at different times for different children. The mechanisms of development responsible for these changes are likely very complex, involving both sensory and cognitive processes. This paper will discuss some previously suggested mechanisms including attention and cue weighting, as well as possibilities suggested from more recent studies in which learning effects were examined. In one task, a simple frequency discrimination was required, while in another the listener was required to extract regularities in complex sequences of sounds that varied from trial to trial. Results suggested that the ability to select and consistently employ an effective listening strategy was especially important in the performance of the more complex task, while simple stimulus exposure and motivation contributed to the simpler task. These factors are important for understanding the perceptual development and for the subsequent application of psychoacoustic findings to clinical populations. [Work supported by the NSERC and the Canadian Language and Literacy Research Network.]
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".