Second-order motion alone does not convey ordinal depth information
Bibliographic record
Abstract
There is convincing evidence that in addition to temporal variation in luminance, the perception of motion can be mediated by temporal changes in contrast. The characteristics of contrast based second-order motion processing are well documented; however, what this information is used for is unknown. Hedge et al. (2004, Journal of Vision, 4–10) argue that second-order motion provides a signal for ordinal depth. While their results are consistent with this proposal, they do not show that second-order cues alone are sufficient to support this percept. In the following experiments we test the hypothesis that the percept of depth ordering in the displays used by Hedge et al. (2004) is due to the presence of luminance based motion cues or high-level figure-ground cues. Here, we asked observers to judge the relative depth of two regions within a rectangular stimulus using solely second-order motion cues, and/or high-level configural (ie. figure-ground) cues. The stimulus consisted of a temporally resampled binary texture moving relative to a mean luminance matched background. Observers were asked to indicate which surface (upper or lower) they perceived as being “nearer”. Our results show that when given second-order motion cues in isolation, observers rely on high-level configural cues. That is, their percept of depth ordering is determined by the location of the textured regions, e.g. when the texture was located in the upper portion of the stimulus, observers reported the upper region to be nearer on 70% of the trials (n = 11), and vice versa. When both regions of the display were filled with temporal noise with different element sizes this configural cue was eliminated and the near/far percept oscillated about 50%. These results show that second-order motion signals in isolation do not support depth ordering. To obtain reliable relative depth percepts from second-order motion additional cues such as accretion/deletion and common motion are needed.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".