<i>Preparing for Speed</i>. Focus on “Preparatory Activity in Premotor and Motor Cortex Reflects the Speed of the Upcoming Reach”
Bibliographic record
Abstract
It is generally accepted that movements can be prepared before their execution, but what preparation entails is not precisely understood. Some computational models suggest that preparation involves the optimization of a “desired trajectory” that describes the movement in its entirety, prior to onset, and is then read-out during overt performance (e.g., Flash and Hogan 1985; Tanaka et al. 2006; Uno et al. 1989). At the other extreme, some models suggest that preparation only describes some simple aspects of the movement and that the details unfold on-line (e.g., Bullock and Grossberg 1988; Feldman 1974; Shadmehr and Wise 2005). In the context of these theoretical proposals, it is interesting to ask: what aspects of an intended movement can be prepared ahead of movement onset? A large number of neurophysiological studies have addressed this question by examining the activity of cells in the reach system during an instructed delay period, between presentation of information specifying a reaching movement and the “go ” signal instructing its onset. These studies have consistently found that neural activity during the delay period accurately predicts the intended direction of movement (Alexander and Crutcher 1990; Kalaska and Crammond 1995; Kurata
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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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.015 |
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".