The bicycle illusion: Sidewalk science informs the integration of motion and shape perception.
Bibliographic record
Abstract
The authors describe a new visual illusion first discovered in a natural setting. A cyclist riding beside a pair of sagging chains that connect fence posts appears to move up and down with the chains. In this illusion, a static shape (the chains) affects the perception of a moving shape (the bicycle), and this influence involves assimilation (averaging) rather than opposition (differentiation). These features distinguish the illusion from illusions of motion capture and induced motion. The authors take this bicycle illusion into the laboratory and report 4 findings: Naïve viewers experience the illusion when discriminating horizontal from sinusoidal motion of a disc in the context of stationary curved lines; the illusion shifts from motion assimilation to motion opposition as the visual size of the display is increased; the assimilation and opposition illusions are dissociated by variations in luminance contrast of the stationary lines and the moving disc; and the illusion does not occur when simply comparing two stationary objects at different locations along the curved lines. The bicycle illusion provides a unique opportunity for studying the interactions between shape and motion perception.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".