Spatial dependency of objects, but not scene gist contributes semantic guidance of attention
Bibliographic record
Abstract
Previous studies (Hwang et al., 2011; Wu et al., 2013) have shown that, during natural scene viewing, observers' gaze transitions are biased towards objects that are semantically similar to the currently fixated one, and this bias does not disappear even when the information about scene gist is removed from the scene. This result, however, does not explain the role of scene gist and how it may interact with spatial dependency among objects. To answer these questions, subjects were asked to view displays in which the presence of scene gist and spatial dependency was varied. Each display was generated by segregating 15 objects from a natural scene in the LabelMe database and pasting them on a grey canvas. To vary spatial dependency, the objects were placed either at the same coordinates as in the original scene (fixed condition), or at randomly selected locations on the canvas (scrambled condition). In the fixed condition, scene gist information was either eliminated or provided by either previewing the original scene for 80 msec, or showing the original scene with all 15 selected objects being marked and subjects being asked to only focus on these objects. The results show that, without scene gist, spatial dependency among objects can still induce semantic guidance, and this effect did not disappear even for saccade amplitudes of up to 16°. Interestingly, the effect of semantic guidance was not affected by providing scene gist information, and it disappeared only when spatial dependency was eliminated. Our results imply that observers mainly use spatial dependency among objects but not scene gist to infer semantic information from the scene and guide their attention. Extracting semantic information simply based on spatial dependency may be an efficient strategy that only adds little cognitive load to the viewing task. Meeting abstract presented at VSS 2014
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".