Humans can smoothly pursue but fail to intercept accelerating targets
Bibliographic record
Abstract
The ability to accurately judge the acceleration of moving objects is critical to our survival. Whereas the perceptual system is surprisingly insensitive to acceleration, humans can accurately track accelerating targets with smooth pursuit eye movements. When the target is briefly occluded, predictive pursuit scales with target acceleration, indicating that the oculomotor system forms an acceleration-based prediction of target motion. Here we ask whether acceleration is taken into account when manually intercepting accelerating targets. Participants (n=16) viewed a small disk that moved along a horizontal path with one of four constant, linear levels of acceleration (-8,-4,+4,+8 m/s/s). The target was shown for 800 ms before temporary occlusion. Target velocity was always 20°/s at the time of occlusion, allowing us to test whether participants based their interception on the final target velocity before occlusion, or on continuous target acceleration. Participants had to predict the time of target reappearance by manually intercepting it with a quick pointing movement of their right hand. We recorded participants’ eye and 3D-hand position using an EyeLink 1000 eye tracker and a trakSTAR electromagnetic motion tracking system. The correspondence between target acceleration and eye (smooth pursuit acceleration) or hand (interception timing) were assessed using linear regression. Pursuit acceleration closely matched target acceleration (median slope = .83; 95% CI = [.73, 1.27]). In contrast, participants did not take acceleration into account when timing their manual interception (median slope = -.33; 95% CI = [-.46, -.07]), yielding systematic interception errors–too early for decelerating targets and too late for accelerating targets. Our results show that the oculomotor system can rely on continuous sampling of the target motion, yielding a pursuit response sensitive to target acceleration. Yet, humans might be limited in their ability to predict accelerating targets for hand movement control, relying on the final target velocity sample prior to occlusion.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".