Inverting the Facing-the-Viewer Bias for Biological Motion Stimuli
Bibliographic record
Abstract
Depth-ambiguous point-light walkers are most frequently seen as facing-the-viewer (FTV). It has been argued that the FTV bias depends on recognising the stimulus as a person. Accordingly, reducing the social relevance of biological motion by presenting stimuli upside down has been shown to reduce FTV bias. Here, we replicated the experiment that reported this finding and added stick figure walkers to the task in order to assess the effect of explicit shape information on facing bias for inverted figures. We measured the FTV bias for upright and inverted stick figure walkers and point-light walkers presented in different azimuth orientations. Inversion of the stimuli did not reduce facing direction judgements to chance levels. In fact, we observed a significant facing away bias in the inverted stimulus conditions. In addition, we found no difference in the pattern of data between stick figure and point-light walkers. Although the results are broadly consistent with previous findings, we do not conclude that inverting biological motion simply negates the FTV bias; rather, inversion causes stimuli to be seen facing away from the viewer more often than not. The results support the interpretation that primarily low-level visual processes are responsible for the biases produced by both upright and inverted stimuli.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".