Neural Aspects of Prospective Control through Resonating Taus in an Interceptive Timing Task
Bibliographic record
Abstract
High-density electroencephalography from visual and motor cortices in addition to kinematic hand and target movement recordings were used to investigate τ-coupling between brain activity patterns and physical movements in an interceptive timing task. Twelve adult participants were presented with a target car moving towards a destination at three constant accelerations, and an effector dot was available to intercept the car at the destination with a swift movement of the finger. A τ-coupling analysis was used to investigate involvement of perception and action variables at both the ecological scale of behavior and neural scale. By introducing the concept of resonance, the underlying dynamics of interceptive actions were investigated. A variety of one- and two-scale τ-coupling analyses showed significant differences in distinguishing between slow, medium, and fast target speed when car motion and finger movement, VEP and MRP brain activity, VEP and car motion, and MRP and finger movement were involved. These results suggested that the temporal structure present at the ecological scale is reflected at the neural scale. The results further showed a strong effect of target speed, indicating that τ-coupling constants k and kres increased with higher speeds of the moving target. It was concluded that τ-coupling can be considered a valuable tool when combining different types of variables at both the ecological and neural levels of analysis.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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 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".