Endogenous Neuromagnetic Activity for Mental Hierarchy of Timing
Bibliographic record
Abstract
The frontal-striatal circuits, the cerebellum, and motor cortices play crucial roles in processing timing information on second to millisecond scales. However, little is known about the physiological mechanism underlying human's preference to robustly encode a sequence of time intervals into a mental hierarchy of temporal units called meter. This is especially salient in music: temporal patterns are typically interpreted as integer multiples of a basic unit (i.e., the beat) and accommodated into a global context such as march or waltz. With magnetoencephalography and spatial-filtering source analysis, we demonstrated that the time courses of neural activities index a subjectively induced meter context. Auditory evoked responses from hippocampus, basal ganglia, and auditory and association cortices showed a significant contrast between march and waltz metric conditions during listening to identical click stimuli. Specifically, the right hippocampus was activated differentially at 80 ms to the march downbeat (the count one) and approximately 250 ms to the waltz downbeat. In contrast, basal ganglia showed a larger 80 ms peak for march downbeat than waltz. The metric contrast was also expressed in long-latency responses in the right temporal lobe. These findings suggest that anticipatory processes in the hippocampal memory system and temporal computation mechanism in the basal ganglia circuits facilitate endogenous activities in auditory and association cortices through feedback loops. The close interaction of auditory, motor, and limbic systems suggests a distributed network for metric organization in temporal processing and its relevance for musical behavior.
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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) | 0.002 | 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".