Percepts of biological motion disappear in slow-moving displays: Evidence for domain-specific agent perception
Bibliographic record
Abstract
The most important stimuli we perceive may be other agents, given their direct effects on our fitness. Accordingly, perception may be specialized for processing agents, and one of the best-studied examples may be *biological motion*: displays of surprisingly few moving dots (‘point-light walkers’; PLWs) nevertheless give rise to rich percepts of locomoting agents — even when static frames from such displays appear as meaningless jumbles of dots. Does this reflect a distinct, domain-specific form of perception, or is it (merely) a complex instance of general motion/shape perception? Inspired by the fact that humans tend to move at certain minimum speeds, we explored this by simply asking how *slow* PLWs can be before percepts of biological motion are impaired or destroyed. Are those limits similar to lower-level motion perception thresholds? Or might biological motion have its own domain-specific lower “speed limit”? Observers viewed PLWs moving in place, embedded in noise (with extra irrelevant moving dots). Animations moved at typical speeds, or at speeds that were considerably slower but still very far above motion perception thresholds. And for slow displays, we always tested both duration-matched versions (with less overall motion) and trajectory-matched versions (that simply lasted longer). Across several experiments, observers tried to discriminate various properties — such as the direction of locomotion, the walker’s apparent gender, or even whether a PLW was present or absent in the first place. We always obtained the same results (which were also apparent as powerful phenomenological demonstrations): discrimination of each of these aspects of biological motion was greatly impaired (often to chance level) in the slower displays, despite the fact that the motion was still readily visible. This demonstrates the utility of exploring ‘slow visual cognition’, and supports the characterization of biological motion perception as a domain-specific form of visual processing.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".