Virtual hand actions show behavioral and neural signatures of right-handedness
Bibliographic record
Abstract
Since the advent of virtual reality and video games, people are increasingly performing actions using avatars in simulated environments. We wondered whether such virtual actions with an avatar’s body evoke the same behaviors and neural correlates as real actions with one’s own body. We developed a video game in which a virtual right or left hand, seen from a first-person perspective, could reach to grasp and move a ball from one location to another during functional magnetic imaging (fMRI). In a Play condition, right-handed participants (n=25) used either their right or left hand to control the avatar’s left or right hand using a game controller (joystick/trigger). In a Watch condition, participants were instructed not to use the controller and simply watch a replay of a previous Play run. Behaviorally, participants were more accurate at dropping the ball on a target when using their virtual right vs. left hand, irrespective of the controlling hand used, suggesting that hand dominance in the virtual environment may reflect the hand dominance of the player. Neurally, as expected during Play, we found activation contralateral to the controlling hand. More interestingly, during Watch, we also found higher activation for actions with the virtual left vs. right hand in the right hemisphere. Specifically, virtual left hand actions evoked more right-hemisphere activation in regions in both the ventral stream (the hand area of lateral occipitotemporal cortex) and dorsal stream (reach-selective areas in superior parieto-occipital cortex, superior parietal cortex, and dorsal premotor cortex). Surprisingly, even though right-hand dominance is thought to be a feature of the motor system, our results suggest that even the mere appearance of the hand – whether it looks like a left or a right hand – affects virtual hand actions and their neural correlates.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".