The Foreground Bias: Initial scene representations dominated by foreground information
Bibliographic record
Abstract
Researchers have often posited that scene representations have a hierarchical structure with background elements providing a scaffold for more detailed foreground elements (Brooks, Rasmussen, & Hollingworth, 2010; Davenport & Potter, 2004; Henderson & Hollingworth, 1999). To further investigate scene representation and the role of background and foreground information, we introduced a new stimulus set: chimera scenes, which have the foreground set of objects belonging to one scene category, and the surrounding background structure belonging to another category. Across three experiments, we examined the contribution of each scene plane to the initial understanding of scenes when rapidly presented. In the first experiment, participants categorized either Normal or Chimera scenes (i.e., scenes with background and foreground from different semantic categories). Results revealed a Foreground Bias, in which participants initially processed the foreground information at the exclusion of background information. Interestingly, this bias persisted in Experiment 2 when the initial fixation position within the scene image was modified such that even when fixating the scene background, participants continued to show a Foreground Bias. This was true for the shortest presentation duration (50ms) and dissipated once the presentation duration exceeded 100ms. In Experiment 3, we changed the task to further emphasize the scene category information (for a more global oriented task), but found that the Foreground Bias persisted. We conclude that the Foreground Bias arises from initial processing of scenes for understanding and suggests that attention is initially focused on the foreground and over time expands to include background information. Implications for scene gist perception and scene representation theories will be discussed. Meeting abstract presented at VSS 2018
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".