Foreground Bias: Inconsistent Target Effects Reduced When Searching Across Depth
Bibliographic record
Abstract
Attentional guidance in scenes is influenced by a multitude of factors, some of which operate jointly and some independently. The semantic context, which relates incoming visual properties with prior knowledge, is among the most influential factors. Recent research has demonstrated a strong foreground bias in scene processing, supported by both eye-tracking data (more fixations to foreground), and visual search (faster and more accurate target detection in foreground). However, it is unclear whether this foreground prioritization is influenced by semantic context. Here, we examined how attention was deployed during search, depending on whether the target was consistent with the foreground or background. For each scene, targets were selected to be semantically consistent with either the foreground or background (e.g., toaster in kitchen, printer in office). Targets became inconsistent when swapped between foreground and background. To account for size differences across depth, the visual angle of large objects in the background was comparable to small objects in foreground. Thus, we implemented a fully crossed factorial design with: Depth (foreground vs. background), Consistency (semantically consistent vs. inconsistent), and Size (small vs. large) as within-subjects factors. Participants searched for these targets and response times (RT) were collected. Results indicated significant main effects of depth and consistency, with faster RT for foreground and semantically consistent targets. However, there was also a 2-way interaction of depth and size, and a 3-way interaction. Further analyses of the 3-way interaction revealed faster RT for consistent targets only for small foreground and large background targets. To further control for size, only targets of comparable visual angle were included in a subsequent analysis. Here, the effect of semantic consistency was significantly smaller in the foreground than background region. We conclude the Foreground Bias modulates the effects of semantics by decreasing its impact in space near the viewer.
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How this classification was reachedexpand
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".