Biological Motion Captures Attention
Bibliographic record
Abstract
Across our evolutionary history, detecting potential prey and predators has been a critical aspect of survival. Among the consequences of this evolutionary past, stimuli that resemble animals are preferentially processed; for example, modern humans initiate saccades to, and detect changes more quickly in, pictures of animals over non-animate objects. Animacy is, however, defined by more than just the canonical shapes or forms of animals. Certain types of motion (i.e., biological motion) also signify animacy, and the human visual system may be predisposed to process these types of motion. For example, we can extract surprising amounts of information about walking from just a few points of light. If biological motion is as salient an indicator of animacy as shape or form, can biological motion capture attention? To answer this question, we compared the time to detect targets in objects that were either moving predictably due to collisions with other objects (nonbiological motion) or moving unpredictably with no such collisions (biological motion). All of the experiments used four geometric objects that moved through the display, bouncing into each other or rebounding off the frame in a predictable manner until one object exhibited biological motion. The first two experiments showed that detecting (Exp1a) and identifying (Exp1b) targets were faster in objects exhibiting biological motion. Because previous research has demonstrated that observers rate objects as more animate with greater changes in direction or speed, the basic procedure of the previous experiments was used with the biological motion having three different angles of direction change (Exp2a) or three different changes in speed (Exp2b). Observers again detected targets more quickly in objects that exhibited biological motion, and response times decreased with greater changes in direction or speed. Thus, biological motion, in and of itself, appears to be capable of capturing attention.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".