Mobile version of the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA): Implementation and adult norms
Bibliographic record
Abstract
Timing and rhythm abilities are complex and multidimensional skills that are highly widespread in the general population. This complexity can be partly captured by the Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA). The battery, consisting of 4 perceptual and 5 sensorimotor tests (finger tapping), has been used in healthy adults and in clinical populations (e.g., Parkinson’s disease, ADHD, developmental dyslexia, stuttering), and shows sensitivity to individual differences and impairment. However, major limitations for the generalized use of this tool are the lack of reliable and standardized norms and of a version of the battery that can be used outside the lab. To circumvent these caveats we put forward a new version of BAASTA on a tablet device capable of ensuring lab-equivalent measurements of timing and rhythm abilities. We present normative data obtained with this version of BAASTA from over 100 healthy adults between the ages of 18 and 87 years in a test-retest protocol. Moreover, we propose a new composite score to summarize rhythm capacities, the Beat Tracking Index (BTI), with excellent test-retest reliability. BTI derives from two BAASTA tests (beat alignment, paced tapping), and offers a swift and practical way of measuring rhythmic abilities when research imposes strong time constraints. This mobile BAASTA implementation is more inclusive and far-reaching, while opening new possibilities for reliable remote testing of rhythmic abilities by leveraging accessible and cost-efficient technologies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".