The role of image realism and expectation in illusory self-motion (vection) perception in younger and older adults
Bibliographic record
Abstract
• Examined image realism and expectation as cognitive factors on vection in younger and older adults. • The visual stimulus was either intact (realistic) or scrambled (unrealistic). • Participants were told they are likely (high expectation) or unlikely (low expectation) to experience vection. • Vection was increased overall for the intact image compared to scrambled. • Expectation and age did not significantly influence vection ratings. Research on the illusion of self-motion (vection) has primarily focused on younger adults, with few studies including older adults. In light of documented age differences in bottom-up and top-down perception and attention, the current study examined the impact of stimulus properties (speed), cognitive factors (expectancy), and a combination of both (stimulus realism) on vection in younger (18–35 years) and older (65+ years) adults. Participants were led to believe through manipulation of the study instructions that they were either likely or unlikely to experience vection before they were exposed to a rotating visual stimulus aimed to induce circular vection. Realism was manipulated by disrupting the global consistency of the visual stimulus comprised of an intact 360° panoramic photograph, resulting in two images (intact, scrambled). The speed of the stimulus was varied (faster, slower). Vection was measured using self-ratings of onset latency, duration, and intensity. Results showed that intact images produced more vection than scrambled images, especially at faster speeds. In contrast, expectation did not significantly impact vection. Overall, these patterns were similar across both age groups, although younger adults reported earlier vection onsets than older adults at faster speeds. These findings suggest that vection results from an interplay of stimulus-driven and cognitive factors in both younger and older adults.
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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 | 0.000 |
| 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".