The forest, the trees, or both? Hierarchy and interactions between gist and object processing during perception of real-world scenes
Bibliographic record
Abstract
The global-to-local theories of perception assume that the gist of a scene is computed early and automatically, whereas recognition of objects occurs at a later processing stage, requires attentional resources, and is primed by the representation of gist. To test these theoretical predictions, we investigated the processing hierarchy of gist- and object-recognition and their interaction in two experiments (total N = 60). Backward-masked images of real-world scenes were presented for a range of brief durations - between 8 ms and 100 ms, and participants performed either an object or a background classification task, in separate blocks. We report three main findings. First, scenes’ backgrounds were generally classified more accurately than foreground objects, but recognition of objects was boosted to the same level as backgrounds by cueing spatial attention to the exact object’s location. Second, backgrounds influence objects’ recognition, as objects presented within semantically incongruent backgrounds were classified less accurately. Third, objects influence background categorization, as backgrounds comprising incongruent objects were also classified less accurately. Therefore, the first two findings support the global-to-local theories, implying that gists are indeed more readily perceived than objects, probably at an earlier stage. Yet the latter finding that objects also influence gist recognition suggests a more parallel and interactive view of both processes than previously assumed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".