Control Strategies When Intercepting Slowly Moving Targets
Bibliographic record
Abstract
In 3 experiments, the authors investigated and described how individuals control manual interceptive movements to slowly moving targets. Participants (N = 8 in each experiment) used a computer mouse and a graphics tablet assembly to manually intercept targets moving across a computer screen toward a marked target zone. They moved the cursor so that it would arrive in the target zone simultaneously with the target. In Experiment 1, there was a range of target velocities, including some very slow targets. In Experiment 2, there were 2 movement distance conditions. Participants moved the cursor either the same distance as the target or twice as far. For both experiments, hand speed was found to be related to target speed, even for the very slowly moving targets and when the target-to-cursor distance ratios were altered, suggesting that participants may have used a strategy similar to tracking. To test that notion, in Experiment 3, the authors added a tracking task in which the participants tracked the target cursor into the target zone. Longer time was spent planning the interception movements; however, there was a longer time in deceleration for the tracking movements, suggesting that more visually guided trajectory updates were made in that condition. Thus, although participants scaled their interception movements to the cursor speed, they were using a different strategy than they used in tracking. It is proposed that during target interception, anticipatory mechanisms are used rather than the visual feedback mechanism used when tracking and when pointing to stationary targets.
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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 | 0.006 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".