Visual factors that determine uncertainty in rapid interceptive movements
Bibliographic record
Abstract
The ability to rapidly assess visual motion information is critical for daily tasks that require rapid interceptions such as catching a falling object. When visual information is sparse, interception decisions have to be made under uncertainty. To investigate this in the laboratory, we can constrain the availability of visual information or the time over which information has to be extrapolated. We recorded human observers’ (n=10) eye and hand movements while they viewed the launch of a simulated flyball on a screen. The ball was occluded shortly after launch, and observers had to manually intercept it along its predicted trajectory within a hit zone. We measured interception accuracy in eye and hand movements for different trajectory shapes yielded by ball speed variations. In two sessions, we manipulated uncertainty by either varying ball presentation duration (100-500ms) at constant occlusion duration (500ms), or by varying occlusion duration (100-500ms) at constant presentation duration (500ms). Reducing uncertainty by increasing presentation duration rapidly improved eye and hand interception accuracy and reduced a bias toward the center of the trajectory space that was observed at the shortest presentation duration. These improvements occurred within the first 300ms of ball presentation. By contrast, long occlusion durations yielded a much weaker center-bias. Reducing uncertainty by decreasing occlusion duration from 500-100ms linearly improved interception accuracy until eye and hand interception errors were minimal. The availability of visual information and time available to extrapolate information both determine uncertainty in rapid interceptive control, but they do so at different rates. Effects of presentation duration plateaued early, indicating that 300ms are sufficient to accurately read out visual trajectory information. Occlusion duration improved performance at a later time, as interception switched from predictive to visually-guided control.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| 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".