A study of human performance in recognizing expressive hand movements
Bibliographic record
Abstract
This paper presents a study on human performance in recognizing affective expressions conveyed through movements of hand-like structures. One movement sequence, closing and opening the hand, was performed by a demonstrator in 3 sets of 5 repeated trials, each set intending to convey a different affective expression. Three different expressions, sadness, happiness and anger, were considered. Expressive movement animations were replicated with a human-like hand model, a stick hand model and with a model resembling a palm frond structure. The structures tested have identical kinematics but different physical appearance. The ability of a human to correctly identify the intended expressive movements performed on these different structures was tested with 66 users viewing videos of the animated structures and reporting via an online questionnaire. Results show that anger is reliably perceived by observers from animated movements on different structures, while the other emotions are easily misperceived. The physical appearance of the structure has some impact on perception performance, but was not found to be statistically significant in this study. Furthermore, analyzing the participants' responses in the context of the valence-arousal model of emotion shows that the subjects were able to recognize the arousal component of the affective hand movements across all structures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".