Switching between task control modes incurs greater reprogramming costs than programming the response de novo
Bibliographic record
Abstract
When performing synchronous hand and foot movements, the way the limbs are synchronized differs depending on the mode of control. When performed in a reaction time (RT) paradigm (reactive control), EMG onsets become synchronized resulting in asynchronous displacement onset. However, when the same movement is performed as an anticipation-timing task (predictive control), displacement onset is synchronized by unconsciously introducing a small delay between EMG onsets. The present experiment investigated the reprogramming costs associated with switching between predictive and reaction control modes. Participants (N = 12, 6F) were asked to simultaneously lift their right heel and right hand in an anticipation-timing task when a rotating clock hand reached a specified target. On some trials, an auditory stimulus was presented either 250 ms or 500 ms before the target and participants were instructed to execute the synchronous movement as quickly as possible after the signal (i.e., switch to reactive mode). Results showed that when the auditory stimulus was delivered 250 ms before the target, participants were unable to switch to a reactive control mode but did switch when the auditory stimulus was presented 500 ms before the target. As expected, the RT on switch trials was substantially longer (∼230 ms) than a simple RT control condition but was also significantly longer (∼130 ms) than a choice RT control condition. These results indicate that switching between control modes for a task involving the same musculature incurs reprogramming costs that are even greater than the time required to program the response de novo.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".