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.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".