Bibliographic record
Abstract
One of the most unexpected phenomena of visual search is search asymmetry: the finding that switching the targets and distractors in a search task can drastically change the difficulty of the task. A well-known example is that it is easier to locate a letter C among O's than to locate an O among C's. One proposed explanation for such search asymmetries, based on signal detection theory, is that the internal responses generated by the two targets have different variances. We tested this explanation by measuring ROC curves. Method Three observers detected a C among O's, and vice versa, at contrast threshold, at set sizes one and eight. Observers responded on a six-point confidence rating scale, and we used the rating responses to generate ROC curves. Results At set size one, the slope of the ROC curves indicated approximately equal variances for the internal response distributions of letters C and O. At set size eight, the slope of an ROC curve does not directly indicate the ratio of the standard deviations of the responses evoked by C and O. However, a more careful analysis can still recover this ratio, and indicated that at set size eight, the response distributions for C and O again had approximately equal variances. Conclusions These findings are qualitatively inconsistent with the unequal-variance account of search asymmetry, which requires that the internal response distribution evoked by the easier target, in this case the letter C, has a greater variance. We will consider what alternative theories of visual search are consistent with these results.
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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 | 0.017 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| 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".