Enhanced voice recognition in musicians
Bibliographic record
Abstract
Musicians typically have extensive auditory experience and demonstrate better pitch, timbre, and tempo discrimination compared to non-musicians. Musical training is also correlated with earlier and more robust cortical and subcortical responses to linguistic stimuli. We asked whether musical expertise may contribute to other auditory tasks, namely person and object recognition when both auditory and visual cues to identity are available. Musicians and non-musicians learned face-voice and car-horn "identity" pairs. Using a forced choice, old/new paradigm, participants were tested for recognition of the learned stimuli presented among distractor stimuli under three stimulus conditions (auditory only, visual only, and bimodal audiovisual). Compared to non-musicians, musicians were more sensitive at recognizing voices but not object sounds. Further, voice recognition sensitivity was positively correlated with both years of musical training and hours of weekly practice suggesting an influence of experience on performance. This differential performance for people and object stimuli is consistent with distinct neural substrates for face and object processing. Overall, this study demonstrates that experience in a sensory domain can benefit aspects of that sensory ability, such as voice but not object sound recognition, likely due to plasticity in distinct neural processing pathways.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".