Perception-action coupling in a prediction motion task
Bibliographic record
Abstract
Prediction motion tasks (PMT) involve a participant’s estimation of an object’s location in space and time after visual occlusion of the object’s path (Tresilian, 1995). The purpose of this experiment was to examine two different movement types in the performance of a PMT. Five participants with a mean age of 25.8 years responded to a computer-generated visual display (E-Prime) using a computer mouse. The visual display depicted a ball that was occluded at the midpoint of its trajectory as it traveled toward a target. Participants predicted the arrival of the ball at the target by clicking the mouse (click condition) or by moving the mouse from a point in the ball’s trajectory to the target and clicking upon arrival (move condition). Each response condition was presented under two speed conditions (fast or slow). The time from the onset of the visual display to the mouse click was recorded by E-Prime to calculate the dependent measures. The data were analyzed using a 2 speed (fast/slow) by 2 response type (click/move) repeated measures Analysis of Variance (ANOVA), with constant error (CE) and variable error (VE) as the dependent measures. The CE data revealed a main effect for Response Type whereby the participants in the click condition produced an underestimation error (-309.58 ms) while the move condition produced an overestimation error (475.80 ms). The VE data also revealed a main effect for Response Type with the click condition being significantly more consistent. These results will be discussed as they relate to perception-action coupling.Acknowledgments: We thank Dr. Adrian Popescu for his contributions in the early stages of this project and the Faculty of Physical Education and Recreation's Human Performance Fund.
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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 | 0.002 | 0.022 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".