The Effect of Familiarity on Neural Representations of Music and Language
Bibliographic record
Abstract
We investigated how familiarity alters music and language processing in the brain. We used fMRI to measure brain responses before and after participants were familiarized with novel music and language stimuli. To manipulate the presence of language and music in the stimuli, there were four conditions: (1) whole music (music and words together), (2) instrumental music (no words), (3) a capella music (sung words, no instruments), and (4) spoken words. To manipulate participants' familiarity with the stimuli, we used novel stimuli and a familiarization paradigm designed to mimic "natural" exposure, while controlling for autobiographical memory confounds. Participants completed two fMRI scans that were separated by a stimulus training period. Behaviorally, participants learned the stimuli over the training period. However, there were no significant neural differences between the familiar and unfamiliar stimuli in either univariate or multivariate analyses. There were differences in neural activity in frontal and temporal regions based on the presence of language in the stimuli, and these differences replicated across the two scanning sessions. These results indicate that the way we engage with music is important for creating a memory of that music, and these aspects, over and above familiarity on its own, may be responsible for the robust nature of musical memory in the presence of neurodegenerative disorders such as Alzheimer disease.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".