Perception of box lifting in a point light display paradigm
Bibliographic record
Abstract
Humans are able to perceive unique types of biological motion presented as point light displays (PLDs). Thirty years ago, Runeson and Fryholm (1983) studied observers' perceptions of weights lifted by actors who moved deceptively to feign the object's mass. They identified that, in spite of the lifter's acting, the PLDs provided enough information about the kinematics necessary to lift the object for the observer to form an accurate perception. However, research also shows that PLDs contain information about human expectation and emotion (e.g., Dittrich et al., 1996). This study improves on Runeson and Fryholm's paradigm by having observers judge the weights lifted by a participant who is under the influence of the size weight illusion. The lifter performed lifts of boxes that varied in size (small, medium, large) and weight (25 lbs, 50 lbs, 75 lbs). Moreover, the participants viewed these lifts across 4 different PLD conditions: box-at-rest, moving box, lifter only, box-and-lifter; and 1 full video condition. The results indicate that participants display the least absolute error when judging the full video condition. Furthermore, in the absence of either box or lifter PLD information, the participants' are not able to significantly differentiate the various box weights. These findings suggest that accurate PLD perceptions depend on information about both the actor and the objects. These results are relevant to processes of observational learning and indicate that PLDs may be insufficient for tasks requiring complex kinematic extraction.
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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 | 0.002 |
| 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 | 0.001 |
| 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".