The effect of increasing the complexity of a movement on the motor pathway
Bibliographic record
Abstract
In their seminal experiment, Henry and Rogers (1960) sought to understand how the complexity of a movement affected reaction time (RT). They demonstrated that increasing the number of response elements leads to longer RTs; however, the reason for lengthened RTs has remained controversial. While this phenomenon has been interpreted using neural activation models (e.g. Hanes & Schall, 1996), few studies have examined how changes within the motor pathway may contribute to RT differences. Transcranial magnetic stimulation (TMS) is used to examine responsiveness of the motor pathway by recording motor evoked potentials (MEPs) at the target muscle. Therefore, the purpose of this study was to examine how MEPs were affected by the complexity of a movement in a RT paradigm. Participants (n=12) were seated at a KINARM End-Point Lab and completed a ballistic, simple RT task, in which they directed a robotic handle to one, two or three targets. Across the three levels of complexity, participants completed 8 trials at each TMS point for a total of 144 trials. During each trial, TMS was delivered at 0, 50, 60, 70, 80 or 90% of each participant's mean RT at the stimulator intensity which yielded a triceps brachii MEP equivalent to 10% the maximal M-wave. As intended, RTs increased with increasing movement complexity (p
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".