Comparative visual search: a difference that makes a difference
Bibliographic record
Abstract
Abstract In this article we present a new experimental paradigm: comparative visual search. Each half of a display contains simple geometrical objects of three different colors and forms. The two display halves are identical except for one object mismatched in either color or form. The subject's task is to find this mismatch. We illustrate the potential of this paradigm for investigating the underlying complex processes of perception and cognition by means of an eye‐tracking study. Three possible search strategies are outlined, discussed, and reexamined on the basis of experimental results. Each strategy is characterized by the way it partitions the field of objects into “chunks.” These strategies are: (i) Stimulus‐wise scanning with minimization of total scan path length (a “traveling salesman” strategy), (ii) scanning of the objects in fixed‐size areas (a “searchlight” strategy), and (iii) scanning of object sets based on variably sized clusters defined by object density and heterogeneity (a “clustering” strategy). To elucidate the processes underlying comparative visual search, we introduce besides object density a new entropy‐based measure for object heterogeneity. The effects of local density and entropy on several basic and derived eye‐movement variables clearly rule out the traveling salesman strategy, but are most compatible with the clustering strategy.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".