Visual short-term memory of local information in briefly viewed natural scenes: Configural and non-configural factors
Bibliographic record
Abstract
Typical visual environments contain a rich array of colors, textures, surfaces, and objects, but it is well established that observers do not have access to all of these visual details, even over short intervals (R. A. Rensink, J. K. O'Regan, & J. J. Clark, 1997). Rather, it seems that human vision extracts only partial information from every glance. What is the nature of this selective encoding of the scene? Although there is considerable research on short-term coding of individual objects, much less is known about the representation of a natural scene in visual short-term memory (VSTM). Here, we examine the VSTM of natural scenes using a local recognition task. A major finding is that local recognition performance is better when image segments are viewed in the context of coherent rather than scrambled scenes, suggesting that observers rely on an encoding of a global 'gist' of the scene. Variations on this experiment allow quantification of the role of multiple factors in local recognition. Color statistics and the global configural context are found to be more important than local features of the target, even for a local recognition task.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".