Response-switching costs occur when unconsciously changing the control mode for performing essentially the same task
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, owing to differences in inertial limb properties. However, when the same synchronous 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 whether switching the “mode” of control for a synchronous task would incur a reaction time cost. Participants (n=12) were asked to simultaneously lift both their right heel and right hand in a simple RT paradigm, and in an anticipating-timing task when a rotating clock hand reached a specified target. On a small subset of anticipation-timing trials (16%), an auditory stimulus was presented either 250ms or 500ms before the target and participants were instructed to switch to reactive control and execute the synchronous movement as quickly as possible after the signal. Results showed that when the auditory stimulus was presented 500ms before the target, participants were able to switch to a reactive control mode, although RT was significantly longer compared to when performing the task in a simple RT paradigm. However, when the auditory stimulus was delivered 250ms before the target, participants were unable to switch to a reactive control mode. These results indicate that even when the task is the essentially the same, unconsciously switching between control modes incurs a reaction time cost.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".