Rapid adaptation to acceleration during interceptive hand movements
Bibliographic record
Abstract
Real-world objects in our environment rarely move at a constant speed but usually accelerate or decelerate. Yet, human perception is highly insensitive to visual acceleration. When manually intercepting moving objects, humans commonly ignore acceleration, resulting in systematic interception errors (Kreyenmeier et al., 2022, eNeuro). Here we ask whether humans’ ability to manually intercept accelerating targets improves during repeated exposure to the same rate of acceleration. In a track-intercept task, observers (n=9) tracked the ramp of a small target either moving at constant speed (0 deg/s/s), accelerating (+8 deg/s/s), or decelerating (-8 deg/s/s). After 800 ms, the target disappeared behind an occluder and observers had to rapidly point at the target at the predicted time of reappearance from behind the occluder (time-to-contact; TTC). Observers performed blocks of twelve trials during which they were exposed to the same rate of acceleration. During the first eight trials, the occluder had a fixed width (reference), in the remaining four trials, the occluder was either narrower or wider than the reference (test). In the first trial of each block, observers systematically intercepted too late for accelerating targets and too early for decelerating targets, indicating that they did not take acceleration into account. Within the first four reference trials, they adjusted the timing of their hand movement to match veridical target TTC. In test trials, observers only partially accounted for acceleration and showed similar biases as in early reference trials. Our results show that humans can rapidly adjust the timing of their hand movement to intercept accelerating targets. However, their ability to transfer this adjustment to new TTC conditions is limited. These findings provide further evidence for the inability to decode accelerating motion and to accurately interact with accelerating objects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".