The Perception of Pitch Contours in Typically Developing Children with and without Musical Training
Bibliographic record
Abstract
Background and Aim: Musical training causes neuroplasticity changes which are transferred to other modalities like- audition, cognition. All the musical tests use musical stimuli, which can be challenging for children without musical training due to the unfamiliarity of the stimuli. Dynamic stimuli like pitch contours, mimic musical stimuli. Hence the present study aimed to investigate the perception of pitch contour for different tonal stimuli in typically developing children with and without musical training. Methods: Children aged 9–13 years were categorized into two groups: Group I (with formal musical training) and Group II (without musical training). Musical abilities were assessed using the Montreal Battery for Evaluation of Music Abilities (MBEMA) test, with melody, rhythm, and memory subtests. The melody and rhythm subtests had discrimination of musical tones, while the memory subtest had identification of familiar melodies from previous subtests. Pitch contours for tonal stimulus were generated using PRAAT software. These contours consisted of tone sweeps representing nine patterns (rising, rising-flat, rising-falling, flat, flat-rising, flat-falling, falling, falling-flat, and falling-rising) for 500 Hz, 1, and 2 kHz tones. Children were familiarized with these contours and tested using closed- set identification task using DMDX software. Results: Group I outperformed Group II in both musical ability and pitch contour identification tests. MANOVA revealed significant differences in MBEMA and pitch contour identification between the groups. Conclusion: The contour perception of the different pitch shows evident differences induced by musical training. It is proposed to assess the musical ability of the individual with the tonal pitch contours.Keywords: Perception; pitch contours; musical training
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".