Effector-independent brain network for auditory-motor integration: fMRI evidence from singing and cello playing
Bibliographic record
Abstract
Abstract Many everyday tasks share high-level sensory goals but differ in the movements used to accomplish them. One example of this is musical pitch regulation, where the same notes can be produced using the vocal system or a musical instrument controlled by the hands. Cello playing has previously been shown to rely on brain structures within the singing network for performance of single notes, except in areas related to primary motor control, suggesting that the brain networks for auditory feedback processing and sensorimotor integration may be shared (Segado et al. 2018). However, research has shown that singers and cellists alike can continue singing/playing in tune even in the absence of auditory feedback (Chen et al. 2013, Kleber et al. 2013), so different paradigms are required to test feedback monitoring and control mechanisms. In singing, auditory pitch feedback perturbation paradigms have been used to show that singers engage a network of brain regions including anterior cingulate cortex (ACC), anterior insula (aINS), and intraparietal sulcus (IPS) when compensating for altered pitch feedback, and posterior superior temporal gyrus (pSTG) and supramarginal gyrus (SMG) when ignoring it (Zarate et al. 2005, 2008). To determine whether the brain networks for cello playing and singing directly overlap in these sensory-motor integration areas, in the present study expert cellists were asked to compensate for or ignore introduced pitch perturbations when singing/playing during fMRI scanning. We found that cellists were able to sing/play target tones, and compensate for and ignore introduced feedback perturbations equally well. Brain activity overlapped for singing and playing in IPS and SMG when compensating, and pSTG and dPMC when ignoring; differences between singing/playing across all three conditions were most prominent in M1, centered on the relevant motor effectors (hand, larynx). These findings support the hypothesis that pitch regulation during cello playing relies on structures within the singing network and suggests that differences arise primarily at the level of forward motor control. Highlights Expert cellists were asked to compensate for or ignore introduced pitch perturbations when singing/playing during fMRI scanning. Cellists were able to sing/play target tones, and compensate for and ignore introduced feedback perturbations equally well. Brain activity overlapped for singing and playing in IPS and SMG when compensating, and pSTG and dPMC when ignoring. Differences between singing/playing across were most prominent in M1, centered around the relevant motor effectors (hand, larynx) Findings support the hypothesis that pitch regulation during cello playing relies on structures within the singing network with differences arising primarily at the level of forward motor control
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".