Programming strategies for rapid aiming movements under simple and choice reaction time conditions
Bibliographic record
Abstract
Increases in reaction time (RT) as a function of response complexity have been shown to differ between simple and choice RT tasks. Of interest in the present study was whether the influence of response complexity on RT depends on the extent to which movements are programmed in advance of movement initiation versus during execution (i.e., online). The task consisted of manual aiming movements to one or two targets (one- vs. two-element responses) under simple and choice RT conditions. The probe RT technique was employed to assess attention demands during RT and movement execution. Simple RT was greater for the two- than for the single-target responses but choice RT was not influenced by the number of elements. In both RT tasks, reaction times to the probe increased as a function of number of elements when the probe occurred during movement execution. The presence of the probe also caused an increase in aiming errors in the simple but not choice RT task. These findings indicated that online programming was occurring in both RT tasks. In the simple RT task, increased executive control mediated the integration between response elements through the utilization of visual feedback to facilitate the implementation of the second element.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".