Components of the curveball illusion: Independent contributions of carrier and envelope
Bibliographic record
Abstract
Purpose: The curveball illusion (Shapiro et al., 2008) demonstrates a failure of the peripheral visual field to integrate motions without loss of location information. We were interested in examining how the two motion components independently influence the illusion strength across the visual field. Methods: We used a linear version of the illusion, with a starting point 9 deg horizontally to the right of fixation and apparent trajectories in the combinations of up/down and left/right directions. In each case, the speed of the carrier motion or that of the envelope motion was varied. Illusion strength was measured in terms of the perceived angle of the trajectory with respect to vertical. Illusion strength was also measured for the right/up direction for horizontal eccentricities from fixation (0 deg) to 15 deg for both speed variations. Results: Illusion strength was logarithmically related to speed for each component, and this relationship was very similar in all quadrants. Illusion strength also increased logarithmically over the whole range of eccentricities, with no illusion for any speed at fixation. Already at the smallest eccentricity (1 deg) there was a sizable illusion that varied with speed. Illusion strength saturated at around 15 deg eccentricity. Conclusion: The curveball illusion is well-described in terms of the component motion speeds. With constant stimulus size, illusion strength increases sharply and then saturates with increasing eccentricity. Thus, loss of location information occurs across the whole visual field, except in the very center. Shapiro A. et al. (2008). Meeting of Society of Neuroscience.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".