The role of cognitive factors and personality traits in the perception of illusory self-motion (vection)
Bibliographic record
Abstract
Vection is a perceptual phenomenon that describes the visually induced subjective sensation of self-motion in the absence of physical motion. Previous research has discussed the potential involvement of top-down cognitive mechanisms on vection. Here, we quantified how cognitive manipulations such as contextual information (i.e., expectation) and plausibility (i.e., chair configuration) alter vection. We also explored how individual traits such as field dependence, depersonalization, anxiety, and social desirability might be related to vection. Fifty-one healthy adults were exposed to an optic flow stimulus that consisted of horizontally moving black-and-white bars presented on three adjacent monitors to generate circular vection. Participants were divided into three groups and given experimental instructions designed to induce either strong, weak, or no expectation with regard to the intensity of vection. In addition, the configuration of the chair (rotatable or fixed) was modified during the experiment. Vection onset time, duration, and intensity were recorded. Results showed that expectation altered vection intensity, but only when the chair was in the rotatable configuration. Positive correlations for vection measures with field dependence and depersonalization, but no sex-related effects were found. Our results show that vection can be altered by cognitive factors and that individual traits can affect the perception of vection, suggesting that vection is not a purely perceptual phenomenon, but can also be affected by top-down mechanisms.
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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) | 0.001 | 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".