Object spatial certainty as a measure of spatial variability and its influence on attention
Bibliographic record
Abstract
Some objects have specific places where you can expect them to be found (e.g., toothbrush), while others vary widely (e.g., cat). Previous studies have pointed to the importance of the spatial associations between objects and scenes in informing search strategies. However, the assumptions about objects having a specific location that they are typically found does not take into account the variability inherent in the spatial associations of objects. In the current study, we proposed a new way of measuring this variability and investigated its effects on attention and visual search. First, we developed the Object Spatial Certainty Index by having participants rate where 150 objects were expected to be found in scenes; the index provides a relative measure that ranks these objects from the most spatially predictable (almost always found in one region of the scene, e.g., boots) to the least spatially predictable (equally likely to be in every region of the scene, e.g., plant). In two experiments, we examined how these variations affected search by manipulating whether the targets were either High Certainty or Low Certainty. Our findings demonstrate that the variability of spatial association of objects significantly affected how effectively scene context influences search performance.
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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 | 0.003 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".