Changes in camera elevation dictate perception of point-light walkers' facing direction.
Bibliographic record
Abstract
The degree of perspective distortion of an object depends on the ratio of its size to its distance from the rendering camera (the field-of-view, FOV). Previously, researchers have reported that sufficient amounts of linear perspective can disambiguate the direction of an otherwise depth-ambiguous point-light display (e.g., Schouten & Verfaillie, 2010). Based on their finding that the effect of FOV on the FTV bias is modulated by the height of the camera above ground Troje, Kenny, and Weech ( 2013) hypothesised that this observation is not based on linear perspective per se, but rather the result of a bias to see the walkers feet from above rather than from below. Here, we test explicitly if the previously reported effects of linear perspective are caused by camera elevation changes that pit a facing the viewer bias (FTV) against a very strong viewing from above bias. We asked participants to indicate the perceived facing direction of point-light displays, and modified the camera elevation according to a staircase procedure targeting the 25%, 50% and 75% FTV thresholds. A univariate ANOVA showed that camera elevation caused large changes in perceived facing direction at the three FTV bias thresholds: 25% (M = -12.87°, SD = 10.14°), 50% (M = -6.27°, SD = 10.99°), or 75% (M = -1.00°, SD = 8.28°), F(2, 22) = 16.04, p <.001. Increasing amounts of negative elevation, below the horizontal plane, led to the perception of point-light displays as facing away from the viewer. Most importantly, the resulting psychometric function is identical to those obtained with linear perspective methods that incidentally modify camera elevation (Troje, Weech, & Kenny, 2013). We argue that camera elevation, not linear perspective, produces the previously observed modifications of the facing-the-viewer bias of depth ambiguous point-light walkers. Meeting abstract presented at VSS 2014
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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 | 0.004 |
| 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 | 0.000 |
| 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".