Vibrotactile discrimination of musical timbre.
Bibliographic record
Abstract
Five experiments investigated the ability to discriminate between musical timbres based on vibrotactile stimulation alone. Participants made same/different judgments on pairs of complex waveforms presented sequentially to the back through voice coils embedded in a conforming chair. Discrimination between cello, piano, and trombone tones matched for F0, duration, and magnitude was above chance with white noise masking the sound output of the voice coils (Experiment 1), with additional masking to control for bone-conducted sound (Experiment 2), and among a group of deaf individuals (Experiment 4a). Hearing (Experiment 3) and deaf individuals (Experiment 4b) also successfully discriminated between dull and bright timbres varying only with regard to spectral centroid. We propose that, as with auditory discrimination of musical timbre, vibrotactile discrimination may involve the cortical integration of filtered output from frequency-tuned mechanoreceptors functioning as critical bands.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".