Guidance of eye movements during conjunctive visual search: The distractor-ratio effect.
Bibliographic record
Abstract
The distractor-ratio effect refers to the finding that search performance in a conjunctive visual search task depends on the relative frequency of two types or subsets of distractors when the total number of items in a display is fixed. Previously, Shen, Reingold, and Pomplun (2000) examined participants' patterns of eye movements in a distractor-ratio paradigm and demonstrated that on any given trial saccadic endpoints were biased towards the smaller subset of distractors and participants flexibly switched between different subsets across trials. The current study explored the boundary conditions of this tendency to flexibly search through a smaller subset of distractors by examining the influence of several manipulations known to modulate search efficiency, including stimulus discriminability (Experiment 1), within-dimension versus cross-dimension conjunction search and distractor heterogeneity (Experiment 2). The results indicated that the flexibility of visual guidance and saccadic bias exemplified by the distractor-ratio effect is a robust phenomenon that mediates search efficiency by adapting to changes in the relative informativeness of stimulus dimensions and features.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".