Eliminating the Low Prevalence Effect in Visual Search with a Remarkably Simple Strategy
Why this work is in the frame
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Bibliographic record
Abstract
The low prevalence effect in visual search occurs when rare targets are missed at a disproportionately high rate. This effect has enormous significance in health and public safety and has proven resistant to intervention. In three experiments (Ns = 41, 40, 44), we document a dramatic reduction of the effect using a simple cognitive strategy requiring no training. Instead of asking participants to search for the presence or absence of a target, as is typically done in visual search tasks, we asked participants to engage in “similarity search” – to identify the display element most similar to a target on every trial, regardless of whether a target is present. Under normal search instructions, we observed strong low prevalence effects. Using similarity search, we failed to detect the low prevalence effect under identical visual conditions across three experiments.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it