Autodidacticism and Music: Do Self-Taught Musicians Exhibit the Same Auditory Processing Advantages as Formally Trained Musicians?
Bibliographic record
Abstract
Multiple studies have demonstrated that musicians have enhanced auditory processing abilities compared to non-musicians. In these studies, musicians are usually defined as having received some sort of formal music training. One issue with this definition is that there are many musicians who are self-taught. The goal of the current study was to determine if self-taught musicians exhibit different auditory enhancements as their formally trained counterparts. Three groups of participants were recruited: formally trained musicians, who received formal music training through the conservatory or private lessons; self-taught musicians, who learned to play music through informal methods, such as with books, videos, or by ear; non-musicians, who had little or no music experience. Auditory processing abilities were assessed using a speech-in-noise task, a passive pitch oddball task done while recording electrical brain activity, and a melodic tonal violation task, done both actively and passively while recording electrical brain activity. For the melodic tonal violation task, formally trained musicians were better at detecting a tonal violation compared to self-taught musicians, who were in turn better than non-musicians. The P600 evoked by a tonal violation was enhanced in formally trained musicians compared to non-musicians. The P600 evoked by an out-of-key note did not differ between formally trained and self-taught musicians, while the P600 evoked by an out-of-tune note was smaller in self-taught musicians compared to formally trained musicians. No differences were observed between the groups for the other tasks. This pattern of results suggests that music training format impacts auditory processing abilities in musical tasks; however, it is possible that these differences arose due to pre-existing factors and not due to the training itself.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".