Disambiguating the Stream/Bounce Illusion With Inference
Bibliographic record
Abstract
The ‘stream/bounce’ illusion refers to the perception of an ambiguous visual display in which two discs approach each other on a collision course. The display can be seen as two discs streaming through each other or bouncing off each other. Which perception dominates, may be influenced by a brief transient, usually a sound, presented around the time of simulated contact. Several theories have been proposed to account for the switching in dominance based on sensory processing, attention and cognitive inference, but a universally applicable, parsimonious explanation has not emerged. We hypothesized that only cognitive inference would be influenced by the perceptual history of the display. We rendered the display technically unambiguous by vertically offsetting the targets’ trajectories and manipulated their history by allowing the objects to switch from one trajectory to the other up to four times before the potential collision point. As the number of switches increased, the number of ‘bounce’ responses also increased. These observations show that expectancy is a critical factor in determining whether a bounce or streaming is perceived and may form the basis for a universal explanation of instances of the stream/bounce illusion.
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 | 0.001 | 0.011 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".