Comparative visual search: a difference that makes a difference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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