Exploring top-down influences on illusory self-motion perception (vection) in younger and older adults
Bibliographic record
Abstract
Vection, defined as the sensation of self-motion in the absence of physical movement, is a critical component for an immersive Virtual Reality experience. Research in this domain has predominantly focused on strategies to modulate vection by manipulating physical properties of the visual stimulus (bottom-up factors) with younger-to-middle aged adults. That said, little research has been done investigating the role of cognitive (top-down) factors on vection, and further how these manipulations may differentially influence participants of different age groups. In the current study, we investigated the role of expectation and stimulus realism as cognitive factors for vection in a sample of 48 younger and 46 older adults. Through manipulation of the study instructions, participants were led to believe that they were either likely or unlikely to experience vection before they were exposed to a rotating visual stimulus aimed to induce circular vection. Stimulus realism was manipulated by disrupting the global consistency of a naturalistic visual stimulus (360° photograph), resulting in two image conditions (intact, scrambled). The speed of the stimulus was varied (faster, slower) to further investigate the influence of top-down effects under stronger and weaker vection intensities, respectively. During each trial, three measures of vection were recorded: onset time, duration, and intensity. Results indicated that image realism and speed affected vection: intact and faster-moving stimuli substantially increased vection relative to the other stimulus conditions for all measures. An interaction between speed and age showed reduced vection onset times in younger adults compared to older adults, but only for faster moving stimuli. No other age-related differences and no meaningful role of expectation was found. Taken together, our study demonstrates that certain bottom-up and top-down factors can influence vection independently as well as additively, whereas age does not seem to be a major factor for vection.
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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 | 0.000 | 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".