Characterizing a snapshot of perceptual experience.
Bibliographic record
Abstract
What can we perceive in a single glance of the visual world? Although this question appears rather simple, answering it has been remarkably difficult and controversial. Traditionally, researchers have tried to infer the nature of perceptual experience by examining how many objects and what types of objects are not fully encoded within a scene (e.g., failing to notice a bowl disappearing/changing). Here, we took a different approach and asked how much we could alter an entire scene before observers noticed those global alterations. Surprisingly, we found that observers could fixate on a scene for hundreds of milliseconds yet routinely fail to notice drastic changes to that scene (e.g., scrambling the periphery so no object can be identified, putting the center of 1 scene on the background of another scene). In addition, we also found that as observers allocate more attention to their periphery, their ability to notice these changes to a scene increases. Together, these results show that although a single snapshot of perceptual experience can be remarkably impoverished, it is also not a fixed constant and is likely to be continuously changing from moment to moment depending on attention. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".