Perceptual motor integration in a prediction motion task
Bibliographic record
Abstract
Many activities in our daily life require us to interact with moving objects which may become occluded during movements forcing us to make spatial and temporal estimations. Such estimations are components of Prediction Motion Tasks (PMT). Previously (Marchak, et al 2013), using a custom designed ball movement and occlusion setup on a computer screen; we demonstrated differences in mouse click versus mouse move conditions. In the present study, we further examined performance in PMTs collecting data for eye and hand movements. Five participants (M=26yrs, SD=5.6) predicted the arrival of a ball to a target on a computer touchscreen by either clicking the mouse (mouse click) or by using their index finger to track the ball from a start position to the target and touching the screen upon estimated arrival (hand tracking) following occlusion. The targets moved at 3 speeds creating three different viewing and occluded periods (0.5, 0.75 and 1 seconds). Hand movements were recorded by a 3D motion analysis system (Optotrak 3020) at 240Hz and eye movements were monitored by eye tracker (ASL 6000) at 240Hz. Reaction time, movement time and spatial error data were analyzed using a 2 (movement condition) by 3 (ball speed) repeated measures ANOVA. Results revealed that participants were more accurate when the ball speed was slower. In the hand tracking condition, reaction time for the eyes was faster than the hands and resulted in faster movement times. Results will be discussed as they relate to cognitive and clocking strategies in prediction motion task performance.
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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 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".