BAASTA: Battery for the Assessment of Auditory Sensorimotor and Timing Abilities
Bibliographic record
Abstract
The Battery for the Assessment of Auditory Sensorimotor and Timing Abilities (BAASTA) is a new tool for the systematic assessment of perceptual and sensorimotor timing skills. It spans a broad range of timing skills aimed at differentiating individual timing profiles. BAASTA consists of sensitive time perception and production tasks. Perceptual tasks include duration discrimination, anisochrony detection (with tones and music), and a version of the Beat Alignment Task. Perceptual thresholds for duration discrimination and anisochrony detection are estimated with a maximum likelihood procedure (MLP) algorithm. Production tasks use finger tapping and include unpaced and paced tapping (with tones and music), synchronization-continuation, and adaptive tapping to a sequence with a tempo change. BAASTA was tested in a proof-of-concept study with 20 non-musicians (Experiment 1). To validate the results of the MLP procedure, less widespread than standard staircase methods, three perceptual tasks of the battery (duration discrimination, anisochrony detection with tones, and with music) were further tested in a second group of non-musicians using 2 down / 1 up and 3 down / 1 up staircase paradigms (n = 24) (Experiment 2). The results show that the timing profiles provided by BAASTA allow to detect cases of timing/rhythm disorders. In addition, perceptual thresholds yielded by the MLP algorithm, although generally comparable to the results provided by standard staircase, tend to be slightly lower. In sum, BAASTA provides a comprehensive battery to test perceptual and sensorimotor timing skills, and to detect timing/rhythm deficits.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".