Corrective response times in a coordinated eye-head-arm countermanding task
Bibliographic record
Abstract
Inhibition of motor responses has been described as a race between two competing decision processes of motor initiation and inhibition, which manifest as the reaction time (RT) and the stop signal reaction time (SSRT); in the case where motor initiation wins out over inhibition, an erroneous movement occurs that usually needs to be corrected, leading to corrective response times (CRTs). Here we used a combined eye-head-arm movement countermanding task to investigate the mechanisms governing multiple effector coordination and the timing of corrective responses. We found a high degree of correlation between effector response times for RT, SSRT, and CRT, suggesting that decision processes are strongly dependent across effectors. To gain further insight into the mechanisms underlying CRTs, we tested multiple models to describe the distribution of RTs, SSRTs, and CRTs. The best-ranked model (according to 3 information criteria) extends the LATER race model governing RTs and SSRTs, whereby a second motor initiation process triggers the corrective response (CRT) only after the inhibition process completes in an expedited fashion. Our model suggests that the neural processing underpinning a failed decision has a residual effect on subsequent actions. NEW & NOTEWORTHY Failure to inhibit erroneous movements typically results in corrective movements. For coordinated eye-head-hand movements we show that corrective movements are only initiated after the erroneous movement cancellation signal has reached a decision threshold in an accelerated fashion.
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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 | 0.003 | 0.019 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".