The role of avatar fidelity and sex on self-motion recognition
Bibliographic record
Abstract
Avatars are important for games and immersive social media applications. Although avatars are still not complete digital copies of the user, they often aim to represent a user in terms of appearance (color and shape) and motion. Previous studies have shown that humans can recognize their own motions in point-light displays. Here, we investigated whether recognition of self-motion is dependent on the avatar's fidelity and the congruency of the avatar's sex with that of the participants. Participants performed different actions that were captured and subsequently remapped onto three different body representations: a point-light figure, a male, and a female virtual avatar. In the experiment, participants viewed the motions displayed on the three body representations and responded to whether the motion was their own. Our results show that there was no influence of body representation on self-motion recognition performance, participants were equally sensitive to recognize their own motion on the point-light figure and the virtual characters. In line with previous research, recognition performance was dependent on the action. Sensitivity was highest for uncommon actions, such as dancing and playing ping-pong, and was around chance level for running, suggesting that the degree of individuality of performing certain actions affects self-motion recognition performance. Our results show that people were able to recognize their own motions even when individual body shape cues were completely eliminated and when the avatar's sex differed from own. This suggests that people might rely more on kinematic information rather than shape and sex cues for recognizing own motion. This finding has important implications for avatar design in game and immersive social media applications.
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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) | 0.001 | 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 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".