Bootstrapping a prior? Effects of experience on the facing bias in biological motion perception
Bibliographic record
Abstract
Perceptually bistable visual stimuli provide an interesting means to study how the visual system turns the generally ambiguous flow of sensory information into a reasonably stable model of the world. Biological motion point-light displays provide a particularly interesting class of stimuli in this respect. Even though the stimulus itself does not contain any information about its orientation in depth, fronto-parallel projections of a point-light walker are preferentially seen as if the walker is facing the viewer rather than facing away. In two different experiments, we show that the degree of this “facing-the-viewer bias” strongly depends on the amount of exposure an observer previously had with point-light displays. We measure the degree of the facing bias by asking observers to indicate the apparent spin (clockwise or counter-clockwise) of a point-light walker – a method insensitive to a potentially confounding response bias. In the first experiment, we compared the degree of the facing bias between na&ıuml;ve observers and graduate students who work with point-light displays on a daily basis. In the second experiment, we exposed initially na&ıuml;ve observers over the course of several weeks systematically to point-light displays and measure the degree of the facing bias before and after this treatment. In both cases, we observe a substantial increase in facing bias with the amount of expertise the observers had with point-light displays. We discuss these results in the context of a process which sharpens prior expectations by means of self-reinforcement in the absence of information that contradicts the developing prior.
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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 | 0.030 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".