Spatiotemporal influence of colour on scene gist perception
Bibliographic record
Abstract
Humans can perceive the content (gist) in a scene under pre-attention conditions. Previous work has shown that low spatial frequency information dominates this perception (Schyns & Oliva, 1994). In addition, colour pop-out shows that colour can be perceived pre-attentively, and coarse-scale colour is thought to contribute to scene gist, but only when colour is diagnostic (i.e. predictive) of a scene category (Oliva & Schyns, 2000). Here we investigate the spatiotemporal influence of colour on scene gist. Two types of image are used within our study: natural scenes where colour is diagnostic for scene category (e.g. mountains, coastlines), and man-made scenes where colour does not influence scene categorization (e.g. roads, buildings). Subjects performed a two-alternative forced choice task to identify the category of the presented scene for both natural and man-made scenes. Scenes were shown in three different chromatic conditions: normal chromatic, monochromatic, or inverted RG/BY chromatic channels. Chromatic condition and the duration of scene presentation was randomized within and between blocks. Our data show that for short presentation durations ([[lt]]150 ms), accuracy for natural scenes categorization is high for normal chromatic, but low for monochromatic and inverted chromatic channel scenes. In addition, reaction times are fastest for the normal chromatic condition. For the man-made images, accuracy and reaction times are equal in all three chromatic conditions. For presentation durations over 150 ms, accuracy is high and reaction times are low for all three chromatic conditions, irrespective on the type of scene used. Our conclusion is that although colour plays a role in defining the course scale spatial layout used within the initial onset of rapid scene gist perception of diagnostic natural scenes, other features are used in scene gist perception within non-diagnostic scenes categories, and at longer presentation durations.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".