Are we blind to three-dimensional acceleration?
Bibliographic record
Abstract
Accurate information about three-dimensional (3D) motion is essential for interception. Being able to detect changes in the speed of motion is potentially important, as approaching objects are unlikely to maintain constant velocity either by intent, or because of the force of gravity or friction. However, evidence from the interception literature shows that acceleration is not taken into account when judging time-to-contact from looming (i.e. retinal expansion). These data may reflect a curious insensitivity to 3D acceleration, a possibility that has received little empirical attention. As a first step towards a better understanding of this apparent lack of sensitivity, we assessed discrimination thresholds for 3D velocity changes. Observers viewed animations of an approaching object undergoing an increase (acceleration) or decrease (deceleration) in its simulated approach speed over the trial. The stimulus was a thin outline disk that was viewed monocularly, such that looming was the only available cue to motion in depth. On each trial, observers discriminated acceleration sign. We measured psychometric functions for three interleaved average speeds. To discourage observers from using non-relevant cues (e.g. due to regularities in the stimulus and correlations between variables) we randomized the simulated starting and ending distance. Our results show that observers were able to detect acceleration in depth, but their thresholds were very high (about a 25-33% velocity change). While precision did not depend on average velocity, there was a velocity-dependent bias: observers were more likely to report that the object accelerated for higher average approach speeds and vice versa. Thus, observers were sensitive to the acceleration of an approaching object under minimal cue conditions, but they could not completely dissociate speed and acceleration. We will discuss which signals could support monocular discrimination of 3D acceleration and produce the bias we found. Furthermore, we will extend these experiments to consider stereoscopic 3D acceleration. Meeting abstract presented at VSS 2013
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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 | 0.037 | 0.177 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.007 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".