Does the Aubert-Fleischl phenomenon affect perceived object speed in realistic virtual scenes?
Bibliographic record
Abstract
In the so-called Aubert-Fleischl phenomenon, the speed of an object is underestimated when the observer follows it with their gaze [1, 2]. This is one of the reasons why many experiments on the perception of speed require their observers to keep their gaze on a fixation cross rather than allowing them more naturalistic free viewing. However, when motion is presented embedded in a visual scene the relative motion between background and object might counteract any Aubert-Fleischl-like effects. This study therefore investigates whether pursuing a target with one’s gaze leads to an underestimation of its speed even when its motion occurs in a visual scene. To this end, we immersed participants (n = 8) in a 3D virtual scene where they judged the speed of a single target against the speed of a target cloud in a two-alternative forced-choice task. While the single target was on screen, participants were asked to either keep their gaze on a fixation cross or follow the target. Further, the motion intervals were presented either in a fully black environment that provided no relative motion cues or in a virtual office environment were the objects moved against a textured wall. We found no significant differences in PSEs between the pursuit and fixation conditions in either of the environments. Aubert-Fleischl effects therefore appear not to be strong confounds in experiments relating to the perception of speed, that is, it may not be necessary to restrict participants’ viewing behavior. [1] Aubert, H. (1887). Die Bewegungsempfindung. Pflüger, Archiv Für Die Gesammte Physiologie Des Menschen Und Der Thiere, 40(1), 459–480. https://doi.org/10.1007/BF01612710 [2] Wertheim, A. H., & Van Gelder, P. (1990). An acceleration illusion caused by underestimation of stimulus velocity during pursuit eye movements: Aubert-Fleischl revisited. Perception, 19(4), 471–482. https://doi.org/10.1068/p190471
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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 | 0.009 |
| 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 | 0.001 |
| 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".