Stick figures and point-light displays: Effects of inversion on the facing-the-viewer bias
Bibliographic record
Abstract
Depth-ambiguous point-light walkers are most frequently seen as facing-the-viewer (FTV). Inverting the figures considerably reduces this FTV bias (Vanrie et al., 2004). The finding has been used to argue that the FTV bias depends on recognizing the stimulus as a person which is more difficult when the stimulus is inverted. Recent experiments indicate that the FTV bias is largely caused by a bias to perceive depth-ambiguous surfaces as convex (Weech and Troje, 2013). Based on this research, we hypothesized that the effect of inversion on FTV bias arises due to the difficulty with which coherent 3D shape is resolved from inverted point-light walkers. Without this shape, the stimulus appears flat and the convexity bias does not play out. If explicit, coherent shape is provided (as in stick figures) we would expect no effect of inversion on FTV bias. We measured the FTV bias in 30 participants for upright and inverted point-light walkers and stick figures. We depicted stimuli at frontal and three-quarter views and recorded observers perceived facing directions. We defined the FTV bias as the percentage of responses signaling a facing-towards interpretation. Participants accurately chose one of the two veridical interpretations at a rate of over 95% for both stimulus types. We found an interaction between stimulus representation and orientation: The inversion effect for stick figures (44%) was smaller than that for point-light walkers (55%). This result supports our hypothesis to a limited degree. Unexpectedly, both stimulus types generated reliable facing-away bias when inverted. Results are consistent with the hypothesis that the lower part of the stimulus takes precedence when subjects are making judgments of facing directions, given that the knees and elbows are opposing in terms of the facing direction implied when assumed to be convex. 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 | 0.002 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.005 | 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".