Task-specific temporal prediction mechanisms revealed by motor and electroencephalographic indicators
Bibliographic record
Abstract
• Different mechanisms of temporal prediction coexist on the same temporal scale. • One temporal prediction is intrinsically linked to the motor movement. • EEG-visible temporal preparation is not intrinsically linked to motor movement. • Temporal prediction affects movement execution more than its initiation. Time prediction is pervasive, and it is unclear whether it is supra-modal or task-specific. This study aimed to investigate the role of motor temporal prediction in preparing to stop a movement following a sensory stimulus. Participants performed a straight-line movement with their finger until a target signal, which occurred after a short or long foreperiod. In one task, participants changed movement direction between trials (multidirectional task), while in the other, they always moved in the same direction (unidirectional task). The motor trajectory and EEG signals were continuously recorded. During the foreperiod, participants slowed down their movement, reflecting preparation to stop. To assess the influence of motor temporal prediction we examined how a given trial influences performance on the subsequent trial (sequential effect) when the movement changes or stays the same (multi- vs. unidirectional). In the unidirectional task, but not in the multidirectional task we found sequential effects on several behavioural parameters. In contrast, sequential effects were observed in both tasks on EEG results. This study revealed a temporal prediction related to motor movement (behavioural indicators), and a temporal preparation while waiting for the target (EEG indicator). These findings highlight the importance of considering various temporal prediction mechanisms.
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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 | 0.002 |
| 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 | 0.001 |
| 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".