The time-course of real-world scene perception: Spatial and semantic processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Real-world scene perception unfolds remarkably quickly, yet the underlying visual processes are poorly understood. Space-centered theory maintains that a scene's spatial structure (e.g., openness, mean depth) can be rapidly recovered from low-level image statistics. In turn, the statistical relationship between a scene's spatial properties and semantic content allows for semantic identity to be inferred from its layout. We tested this theory by investigating (1) the temporal dynamics of spatial and semantic perception in real-world scenes, and (2) dependencies between spatial and semantic judgments. Participants viewed backward-masked images for 13.3 to 106.7 ms, and identified the semantic (e.g., beach, road) or spatial structure (e.g., open, closed-off) category. We found no temporal precedence of spatial discrimination relative to semantic discrimination. Computational analyses further suggest that, instead of using spatial layout to infer semantic categories, humans exploit semantic information to discriminate spatial structure categories. These findings challenge traditional 'bottom-up' views of scene perception.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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 it