Robust Differences in Time-to-Contact Estimation in Response to a Postural Manipulation
Bibliographic record
Abstract
The classic time-to-contact literature is largely focused on the optic variables that guide human performance in interception tasks. While prior information (e.g., internalized knowledge on the effects of Earth gravity) has received some attention more recently, other types of non-visual factors have been largely disregarded. In this study, we therefore used a simple postural manipulation to investigate how vestibular and somatosensory cues might affect time-to-contact estimates. To this end, we immersed observers in a VR office environment, which provided strong structural cues on its orientation, and showed them targets travelling on parabolic trajectories in the fronto-parallel plane. These targets could travel at different horizontal and initial vertical speeds and disappeared at a random time in the second half of their motion. Participants then had to indicate by button press when these targets would have returned to their initial height. The trajectories could unfold either according to natural gravity or according to an inverted (i.e., upwards) gravity, and participants completed the experiment while standing upright and while lying supine. The virtual environment and stimulus were head-fixed, i.e., the virtual floor was always simulated at the observers’ feet. While we, unexpectedly, found no differences between natural and inverted gravity targets, we did observe a consistent overestimation (i.e., a later button press) of time-to-contact when lying supine in comparison to standing upright. While other studies relate reported postural differences in estimated time-to-contact to gravity (Baurès & Hecht, 2011,Perception 40, 674-681), our findings are unlikely due to an interaction with such Earth gravity prior. Rather, they suggest that non-visual cues can have a substantial impact on estimated time-to-contact even when they are uninformative with regards to the task at hand.
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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 | 0.000 | 0.007 |
| 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 | 0.001 |
| 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".