Human Action Perception is Consistent, Flexible, and Orientation Dependent
Bibliographic record
Abstract
Previous research has found that observers of object-directed human action pay more attention to information regarding hand contact over information regarding spatial trajectories in action, and that processing of trajectory information is disrupted by inversion. However, observers can also flexibly modulate their attention to spatial trajectory depending on the goal or context of the actor. In Experiments 1(a) and 1b of the current research, we directly compared attention with hand and trajectory information across placing and dropping actions in order to determine whether the hand bias is always present or whether flexibility in action perception can attenuate this bias. Results demonstrated that observers attend more to hand information for placing, but attend equally to hand and trajectory information for dropping. Experiment 2 explored the role of the actor's goal in processing spatial trajectory for mimed dropping actions and non-human control stimuli, and the role of goals in the inversion effect. Results indicated that goal relevance increases processing of trajectory information, and that processing of all spatial trajectories in human action is disrupted by inversion, regardless of the actor's goal. The present findings highlight the role of prediction in action perception, and suggest that human action is processed with expertise.
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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 | 0.005 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".