Bibliographic record
Abstract
The present study investigates how human observers understand real-world scenes. Past studies have shown that individuals infer the meaning or gist of a real-world scene within a single glance. The current study examined how much visual information is needed in order to elicit an understanding of a visual scene. Sixty participants were shown a brief presentation of a scene and the amount of scene information shown was manipulated across six experimental conditions, varying from only details at the centre (local features) to the full scene (global features). Local features are objects present in a scene, or can also be visual features such as textures, colours and other surface properties important for understanding a visual scene. Global features ecompass the actual space of the scene, including the geometry, spatial layout, and scene structure. Based on past research, we anticipate scene understanding will occur where global features are available, but not in conditions where only local features are available. However, preliminary results revealed that participants understood the gist of the scene even when minimal features were available to them. This study aims to further current research on scene understanding and the visual features required to comprehend complex visual information in our environment.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".