Recognizing self in puppet controlled virtual avatars
Bibliographic record
Abstract
Recent work in neuroscience suggests that there is a common coding in the brain between perception, imagination and execution of movement. Further, this common coding is considered to allow people to recognize their own movements when presented as abstract representations, and coordinate with these movements better. We are investigating how this ‘own movement effect ’ could be extended to improve the interaction between players and game avatars, and how it might be leveraged to augment players ’ cognition. To examine this question, we have designed and developed a tangible puppet interface and 3D virtual environment that are tailored to investigate the mapping between player and avatar movements. In a set of two experiments, we show that when the puppet interface is used to transfer players’ movements to the avatar, the players are able to recognize their own movements, when presented alongside others ’ movements. In both experiments, players did not observe their movements being transferred to the avatar, and the recognition occurred after a week of the transfer. Since the recognition effect persisted even with these two handicaps, we conclude that this is a robust effect, and the puppet interface is effective in personalizing an avatar, by transferring a player’s own movements to the virtual character.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".