Effects of simulated and perceived motion on cognitive task performance
Bibliographic record
Abstract
Compelling simulated motion in virtual environments can induce the sensation of self motion (or vection) in stationary observers. While the usefulness and functional significance of vection is still debated, the literature has shown that perceived magnitude of vection is lower when observers perform attentionally demanding cognitive tasks than when attentional demands are absent. Could simulated motion and the resulting vection experienced in virtual environments in turn affect how observers perform various attention demanding tasks? In this study therefore, we investigated how accurately and rapidly observers could perform attention-demanding aural and visual tasks while experiencing levels of vection-inducing motion in a virtual environment. Seventeen adult observers were exposed to different levels of simulated motion at virtual camera speeds of 0 (stationary), 5, 10 and 15 m/s in a straight virtual corridor rendered through a Vive-Pro Virtual Reality headset. During these simulations, they performed aural or visual discrimination tasks, or no task at all. We recorded the accuracy, the time observers took to respond to each task, and the intensity of vection they reported. Repeated Measures ANOVA showed that levels of simulated motion did not significantly affect accuracy on either task (F(3,48) = 1.469, p = .235 aural; F(3,48) = 1.504, p = .226 visual), but significantly affected the response times on aural tasks (F(3,48) = 4.320, p = .009 aural; F(3,48) = 0.916, p = .440 visual). Observers generally perceived less vection at all levels of motion when they performed visual discrimination tasks compared to when they had no task to perform (F(2,32) = 13.784, p = .038). This suggests that perceived intensities of vection are significantly reduced when people perform attentionally demanding tasks related to visual processing. Conversely, vection intensity or simulated motion speed can affect performance on aural tasks.
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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 | 0.004 |
| 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 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".