Size judgments of looming targets: Effect of speed, location and the utilization of eye movements
Bibliographic record
Abstract
Purpose: Judging the size of looming objects is important in many daily activities including driving and sports. The aim of the current experiment was to investigate the effect of speed, location, and the use of eye movements, in judging the size of looming targets. Methods: Ten participants (mean age 27.7 ±4) observed a looming target (a vertical bar that appears to come towards the observer) projected on a screen, at a distance of 2m. Participants clicked a button when the size of the looming target matched a previously shown target. Responses for looming targets at five speeds were obtained in random order from one of the following: a central location (0 deg), a series of peripheral locations (−20,−10, 10 and 20 degrees) while fixating a central location or the same peripheral locations but with eye movements toward the looming targets. Eye movements were recorded with a video-based eye tracker. The effect of speed, target location and the use of eye movements on size match estimates was determined using a mixed design four factor ANOVA. A Tukey's post hoc was used for pair-wise comparisons. Results: Higher speeds resulted in larger size match estimates for all target locations (F[4,4935]=20.73; p[[lt]]0.01).The slope of subjects responses (in size) was significantly different from the rate of stimulus change. A size match main effect was found between central and peripheral locations both with and without eye movements (F[4,4935]=25.23; p[[lt]]0.01).The interaction between target location and speed was not significant (F[8,5485]=0.19; p[[gt]]0.05). Conclusions: Looming speed and target location affect the ability to estimate the size of objects. Size judgments are generally seen to be overestimated but not with a constant reaction time. Location differences in responses cannot be explained by the differences in retinal motion cues as similar results were obtained with eye movements.
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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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".