Characterizing a snapshot of perceptual experience.
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
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).
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 | 0.000 | 0.000 |
| 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) | 0.001 | 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