Adjusting visual illusions for differential sensitivity to target size decreases the likelihood of differentiating action from perception.
Bibliographic record
Abstract
Vision researchers have relied on compelling pictorial illusions to argue both for dissociations between action and perception (multiple visual-systems – MVS - proponents) and against them (common visual system – CVS - proponents). A methodological issue that divides these researchers is whether the effects of illusions on action and perception should be adjusted for baseline differences in sensitivity to size, prior to making the comparisons. Here we use Monte-Carlo simulations to explore how adjusting for the response sensitivity function influences the comparison of illusion effects across response modes. We generated data from multivariate distributions based on typical parameters reported in the literature, before adjusted the effects of the illusion using three techniques: index, zero variance, and a Taylor-approximated application of Fieller's theorem. For each unique combination of parameters, we contrasted pairs of unadjusted and pairs of adjusted illusion effects and computed the observed Type I error (alpha) and II error (beta) values. We used a pseudo d' measure to incorporate both alpha and beta into a single metric of efficiency. The zero variance method yielded a small pseudo d' with unacceptably high alpha. The index method yielded the smallest pseudo d', with a moderate alpha combined with high beta. Among the adjustment methods, Fieller's method yielded the best control over alpha, but at the cost of increased beta. When alpha and beta rates were considered together, unadjusted measurements were, surprisingly, the most efficient. Our findings warrant two recommendations: 1) the Fieller-adjusted effects of the illusion in a given response mode are preferable to other adjustment methods; and 2) the unadjusted effects of the illusion should be used to compare between response modes. These analyses imply that ongoing debates between proponents of MVS and proponents of CVS should be based on unadjusted measures of the same illusion for each response mode. Meeting abstract presented at VSS 2018
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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 | 0.022 | 0.178 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".