Time-shifting in virtual reality induces motor but not non-motor temporal recalibration
Bibliographic record
Abstract
Virtual reality (VR) provides a valuable research tool for mimicking real-world conditions while at the same time allowing investigators to study what occurs when sensorimotor feedback loops are manipulated. VR technology is often seen altering the action-perception relationship, but the effects of these VR manipulations on sensorimotor adaptation are understudied. Here we measured whether exposure to novel relationships between action and sensory reaction in VR produces a recalibration of time perception. We asked 32 participants to perform time perception tasks where the interval of a rotating probe was reproduced using a motor or non-motor reproduction method. These time perception tasks were completed pre- and post-exposure to dynamic VR content in a block-counterbalanced order. One group of participants experienced a standard VR task (normal-time), while another group had their real-world movements coupled to the speed of events that occurred in the virtual space (time-shifting). We expected this novel action-perception relationship to affect motor time perception performance, leaving non-motor time perception unaffected. The results indicated a motor-specific adaptation effect such that duration was under-estimated following exposure to time-shifted VR. A dynamic control task that was conducted outside of VR produced similar results to those of the normal-time VR group, confirming the specificity of the time-shifting manipulation. The findings provide valuable insights into the potential impact of VR on sensorimotor recalibration. Understanding this process can be valuable in developing and implementing this technology in rehabilitation practices.
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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 | 0.001 |
| 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) | 0.002 | 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".