Modulating the Foreground Bias: How Scene Knowledge and Depth Structure Guide Visual Search
Bibliographic record
Abstract
When viewing real-world scenes, visual information extends across depth, yet attention is not allocated evenly across this space. The current study examined whether the prioritization of near-space information, known as the Foreground Bias, reflects a fixed attentional tendency or a flexible process that can be modulated by scene knowledge. Across two experiments, participants searched for target objects that appeared in either the foreground or background of scenes that were either semantically coherent (Normal) or composed of mismatched foreground and background regions (Chimera scenes; Castelhano et al., 2019). In Experiment 1, participants located targets in the foreground more quickly and with fewer fixations than those in the background, suggesting a robust Foreground Bias that was not explained by target size. In Experiment 2, a brief scene preview was introduced to allow participants to encode scene structure before search. Although the Foreground Bias persisted across scene types, the preview selectively reduced the magnitude of this bias in Chimera scenes, suggesting participants could strategically direct search toward the semantically relevant region when sufficient context information was available. Together, these findings suggest that the Foreground Bias reflects a strong, default weighting toward near space that can be flexibly adjusted based on prior scene knowledge.
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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 | 0.005 |
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".