Across the planes: Differing impacts of foreground and background information on visual search in scenes
Bibliographic record
Abstract
When searching in real world scenes, our visual system needs to efficiently sift through complex visual information to quickly find the target. One prevailing question is how the visual system prioritizes and processes information to perform tasks effectively. Researchers have postulated that scene representations consist of both background and foreground elements, where background elements provide a scaffold for more detailed foreground elements (Davenport & Potter, 2004; Henderson & Hollingworth, 1999; Munneke, Brentari & Peelen, 2013). Here, we defined background information as boundary defining elements such as walls, floors, ceilings (Oliva & Torralba, 2001) and foreground information as moveable objects nested inside the background (Henderson & Hollingworth, 1999). In the current study, we were interested in how scene information from different planes impacts search for targets (foreground versus background). 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. We posit that differences in how the background and foreground are processed will result in different search strategies across scene planes. Participants performed search in scenes that had either consistent (Normal) or inconsistent (Chimera) foreground and background contexts. Target objects could appear in either the foreground or background of the scene. Results showed an interesting effect of target placement. Although participants had a longer target latency for foreground than background targets, they had a shorter response times, had fewer fixations to the target, and had a more direct scan path to the target when the target was in the foreground. Moreover, participants were able to discern foreground targets from farther in the periphery than background targets. These results suggest that there are processing differences between searches for target in different planes of the scene. Implications for scene representations and search mechanisms will be discussed. Meeting abstract presented at VSS 2018
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".