Conjunction Search Onset Following Single-Feature Preview: Equating Visual Transients
Bibliographic record
Abstract
What happens to visual selection if the features of objects in a scene are viewed incrementally rather than simultaneously? According to Olds et al. (2009), it depends upon which feature is presented first. Olds et al. (2009) used the feature-preview search paradigm to cue conjunction search items by presenting observers with a preview display that contained 1 of 2 features for all of the search items. Prior exposure to some features facilitated subsequent visual selection more than prior exposure to others; overall, size-preview offered the greatest search facilitation, followed by color-preview, and lastly, orientation-preview. Some feature-preview conditions, however, contained luminance transients, while others did not. In the present study, we equated relative differences in luminance onsets, across the different feature-preview conditions in order to determine whether or not feature-preview effects are mediated by luminance transients. The general pattern of results obtained by Olds et al. (2009) was replicated and different featurepreviews continued to have differential effects on subsequent search; relative differences in luminance transients did not mediate feature-preview effects. Alternative theories are proposed and discussed.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".