Does a convexity prior explain the facing-the-viewer bias in the perception of biological motion?
Bibliographic record
Abstract
Point-light walkers generally contain no information about their orientation in depth, yet observers consistently prefer the facing-the-viewer (FTV) interpretation (Vanrie et al., 2004). Some research (Schouten et al., 2011) suggests that local stimulus properties elicit the bias: Presentation of the lower half of point-light figures elicits a pronounced FTV bias, while presentation of the upper half does not. Other research suggests high level causes: Male walkers generate a stronger FTV bias than female walker (Brooks et al., 2004). Interestingly, no FTV biases are observed with human silhouettes (Troje & McAdam, 2010). We hypothesise that the FTV bias is due to a convexity prior (Mamassian & Landy, 1998). Accordingly, the knees afford a FTV interpretation while the elbows, specifically when pointing back (as typical for women) rather than sideways (as in males), afford a facing-away interpretation. This requires visual structures other than occluding contours which are neither concave nor convex with respect to the line of sight. Here we asked observers to indicate perceived rotation directions (clockwise/counter clockwise) of a silhouette of a crouching human figure with knees pointing forward and elbows pointing back. Four conditions were included: silhouettes presented with no markers, with markers at the centre of the knees, with markers at the centre of the elbows, and with markers on both knees and elbows. We measured facing bias as the proportion of depth reversals from the ‘away’ to the ‘toward’ interpretation. The silhouette alone elicited a weak facing bias, as did the silhouette with both elbow and knee markers present. As predicted, silhouettes with knee markers only elicited a FTV bias while elbow markers alone elicited a notable facing away bias. These results help to interpret and unify various findings regarding the FTV bias, and support the idea that an experientially-driven convexity prior guides interpretations of depth-ambiguous figures. 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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".