Zooming in and out: Global-local shifts in large scale visual search
Bibliographic record
Abstract
Search outside of the laboratory often involves movement within the search environment, dynamically engaging the visual array in order to bring a particular region of space into view, or to change the size of an object’s image in the visual field. In three experiments, we introduce a novel search task in which participants search through displays containing up to 128 items arranged on a search ‘canvas’ – potentially much larger than the physical display depending on zoom. To search, participants used the mouse scroll wheel to zoom in and out, and a click-drag interface to move the canvas within the display frame at that zoom level. Detection of the unique target item was signaled by clicking on the location of the item. Throughout each trial we recorded the magnification level and position of the frame in order to reconstruct search characteristics in terms of shifts in magnification and shifts in position. Here, we focus in particular on the number of transitions between global (zoomed out) and local (zoomed in) views during search. In Experiment 1, we manipulated item density. In Experiment 2, we replaced the typical homogeneous distribution of items with discrete clumps of items, and manipulated the number of clumps, density of items within clumps, and density of clumps within the entire search field. In Experiment 3, we varied the relative sizes of the items within individual displays, manipulating the number of distinct sizes (1, 2, or 4), and at which level of size the target was presented. We report increased numbers of global-local transitions with decreasing density, with increasing numbers of clumps, and as the number of distinct item sizes in each display increases. These results are consistent with global-local transitions during search being driven by the need to acquire different information from different levels of zoom. Meeting abstract presented at VSS 2012
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".