Bibliographic record
Abstract
The present study examined how we recognize real‐world scenes. Previous studies suggested that, in a single glance, we could extract enough information from a visual scene to understand a scene’s category (e.g., kitchen, living room, bedroom). However, it is yet unclear what type of visual information leads to this understanding. The experiment investigated whether global information from the whole scene or local information from individual objects is critical. Global information refers to large‐scale, immovable structures in the background of a scene (e.g. kitchen cabinets). Local information refers to smaller‐scale, movable objects in the foreground of a scene (e.g. kitchen table). Participants were briefly presented with a scene in which the set of foreground objects belonged to one scene type, and the background belonged to another scene type. After the presentation of the scene, a name of the target object that was consistent with either the background (e.g. blender in kitchen) or consistent with the background (e.g. coffee table) was presented. Participants decided whether the target object is likely to appear in the scene. If local foreground information was initially used to activate scene gist, there should be a higher response rate towards the foreground‐consistent target. If global background information was initially used, there should be a higher response rate towards the background‐consistent target. Preliminary results suggest that participants used the foreground‐consistent target objects more often. This suggests that we may initially use local information to understand scenes.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".