Temporal Generalization of Synchronized Saccades Beyond the Trained Range in Monkeys
Bibliographic record
Abstract
Synchronized movements with external periodic rhythms, such as dancing to a beat, are commonly observed in daily life. Although it has been well established that some vocal learning species (including parrots and humans) spontaneously develop this ability, it has only recently been shown that monkeys are also capable of predictive and tempo-flexible synchronization to periodic stimuli. In our previous study, monkeys were trained to make predictive saccades for alternately presented visual stimuli at fixed stimulus onset asynchronies (SOAs) to obtain a liquid reward. The monkeys generalized predictive synchronization to novel SOAs in the middle of trained range, suggesting a capacity for tempo-flexible synchronization. However, it is possible that when encountering a novel tempo, the monkeys might sample learned saccade sequences from those for the short and long SOAs so that the mean saccade interval matched the untrained SOA. To eliminate this possibility, in the current study we tested monkeys on novel SOAs outside the trained range. Animals were trained to generate synchronized eye movements for 600 and 900-ms SOAs for a few weeks, and then were tested for longer SOAs. The accuracy and precision of predictive saccades for one untrained SOA (1200 ms) were comparable to those for the trained conditions. On the other hand, the variance of predictive saccade latency and the proportion of reactive saccades increased significantly in the longer SOA conditions (1800 and 2400 ms), indicating that temporal prediction of periodic stimuli was difficult in this range, similar to previous results on synchronized tapping in humans. Our results suggest that monkeys might share similar synchronization mechanisms with humans, which can be subject to physiological examination in future studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".