Effects of movement-shape inconsistencies on perceived weight of lifted boxes.
Bibliographic record
Abstract
Perceiving the weight of a lifted object from visual displays of the lifting person is a non-trivial task. Runeson and Frykholm (1981), who worked with biological motion point-light displays, attributed the ability to estimate the weight of a lifted box to what they called the Kinematic Specification of Dynamics. The KSD assumes that dynamics are inferred from observed kinematic patterns by means of an internal model of the relations between body shape and body kinematics. Using MoSh, that is, Motion and Shape Capture from Sparse Markers (Loper, Mahmood, & Black, 2014) we created animated, life-like human avatars from surface motion capture data of performers lifting light and heavy boxes. For some of our stimuli, we then combined the body shape of one lifter with the kinematics of another to create hybrid lifters. In the consistent condition, stimuli were generated using the shape and movement from the same performer. In the low- and high- inconsistency conditions, the shape and movements of the stimuli were taken from different performers; however, in the former, the shape and motion were from different performers with similar body masses, and in the latter, shape was matched with motion from individuals with dissimilar body masses. Participants estimated the perceived weight of the lifted box. Results showed that participants could discriminate between box weights, although they slightly overestimated their real weight. However, we did not find the expected dependency of internal consistency. Further studies will examine the degree to which larger inconsistencies are detectable, and in which domains internal consistency matters. Meeting abstract presented at VSS 2016
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".