Bibliographic record
Abstract
Many animals can encode temporal intervals and use them to plan their actions, but only humans can flexibly extract a regular beat from complex patterns, such as musical rhythms.Beat-based timing is hypothesized to rely on the integration of sensory information with temporal information encoded in motor regions such as the medial premotor cortex (MPC), but how beat-based timing might be encoded in neuronal populations is mostly unknown.Ga ´mez and colleagues show that the MPC encodes temporal information via a population code visible as circular trajectories in state space; these patterns may represent precursors to more-complex skills such as beat-based timing.Just listen to Antonio Carlos Jobim's "Girl from Ipanema" (YouTube version of this song available here).The urge to sway with the languid, syncopated samba rhythm is irresistible.Why does this happen?It somehow must result from interactions between the auditory and motor systems of the brain that detect and anticipate temporally predictable moments in the song.This beat-based form of sensory-motor timing allows humans to flexibly extract a regular temporal structure from a range of rhythms, from simple isochronous sequences, in which all the intervals are identical, to more-complex meters like those of waltzes, marches, and sambaslike Jobim's piece [1,2].People even perceive a regular beat at moments in the music when no sound is present [3], as in the last bar of "Girl," when the downbeat occurs on a silent rest.Further, motor regions of the brain are active when people listen to musical rhythms, even without moving [4].These facts demonstrate that the beat is an abstract percept that is not solely dependent on features of the stimulus or motor responses.Beat-based structure is particular to music, but quasiperiodic temporal structure is also present at longer timescales in language [5].Humans can use temporal predictions based on the beat to facilitate action and perception.Predictable beats enhance attention to stimuli that fall on the beat, resulting in better discrimination or detection [6,7].Predictable beats also facilitate movement timing, making it more accurate and less variable [8].This may be the reason why athletes in many sports use music to guide movement timing.Beat-based timing can be contrasted with interval timing-the ability to encode and remember the interval between two events [9].Many animals can encode temporal intervals and use them to plan their actions [10].Your dog knows when it is time for dinner and how long it takes to pour the food.In contrast, beat-based timing appears to be characteristically
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".