Distinct and Shared Neural Basis Underlying Music and Language:A Perspective from Meta-analysis
Bibliographic record
Abstract
Music and language are arguably the most characteristic traits of human beings. On one hand, previous functional magnetic resonance imaging(fMRI) studies have identified multiple cortical regions that are involved in the processing of both music and language, suggesting shared neural substrate for music and language. On the other hand, neuropsychological studies on brain-lesioned patients show the double dissociation between music and language, suggesting distinct neural substrates for music and language respectively. Here we used meta-analysis to examine the relation of the neural basis underlying music and language. First, we adopted the data from three meta-analysis studies on music and language respectively. Specifically, for the neural substrate of music, we focused on two processing levels specific to music processing, which were interval analysis(15 contrasts and 63 peaks) and structure analysis(19 contrasts and 217 peaks)(Lai, Xu, Song, Liu, 2013). For the neural substrate of language, three processing levels specific to language processing were selected, which were phonological analysis(86 contrasts and 344 peaks), lexico-semantic analysis(111contrasts and 339 peaks), and sentence analysis(65 contrasts and 218 peaks)(Vigneau et al., 2006; Vigneau et al., 2011). Second, we projected these peak activation elicited by processing either music or language onto the MNI(Montreal Neurological Institute) space to visualize the distribution of cortical regions involved in music and language with Caret. Finally, to explore the relation of neural substrates underlying music and language, we calculated whether pairs of peak activation were spatially overlapped or dissociated by K-means clustering and multivariate analysis of variance(MANOVA). The overlapping rate was estimated to quantify the extent to which music and language shared common neural substrates. We finally found 11 pairs of overlapping clusters. Music and language shared neural substrates at all levels of processing tested. Specially, the overlapped clusters from phonological processing of language and music perception were mainly in the auditory-motor loop, the overlapped clusters from semantic processing and music perception were in core loop, and the overlapped clusters of sentence processing and music were in cognition-emotion loop. In addition, the neural substrate underlying interval analysis of music overlapped with that underlying language processing as much as 50% in left hemisphere, which was mainly in the left superior temporal gyrus, left precentral gyrus, left pars triangularis of the inferior frontal gyrus and right insular. The neural substrate underlying structure analysis of music overlapped with that underlying language processing as much as 7% and 14% in the left and right hemisphere respectively, which was mainly in left Rolandic operculum, right pars opercularis of the inferior frontal gyrus and right insular. In sum, our study illuminates the functionality of the distinct and shared neural substrates underlying music and language. That is, for lower-level processes, such as interval analysis, phonological analysis and lexico-semantic analysis, music and language are more likely to share the same neural substrate in auditory analysis, auditory-motor integration and working memory. By contrast, for higher-level processes, especially in the structure analysis and sentence analysis, the neural substrate underlying music is more likely distinct from that underlying language. Models were proposed to illustrate the distinct and shared neural mechanisms underlying music and language, which invites future studies on the relation between music and language.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".