Removing non-accidental properties increases the duration of object awareness
Bibliographic record
Abstract
While countless experiments have investigated the properties and conditions that affect initial object recognition, little is known about how the awareness of objects is maintained after successful recognition. Substantial evidence demonstrates that vertices, defined as points of cotermination, are crucial properties for object recognition. Removing the vertices or “non-accidental properties” of objects impairs recognition speed and accuracy more than when the vertices are left intact. Here, we tested whether removing vertices from fragmented objects also impaired the maintenance of object awareness using a shape-from-motion (SFM) paradigm. Fragmented line-drawings of objects were rotated relative to a background of randomly-oriented lines. After the motion is stopped, the percept of the object persists briefly. Participants made a response to indicate when they were able to identify the object, as well as when their subjective awareness for the object had disappeared. Object vertices were either removed or left intact. We confirmed earlier studies demonstrating that objects without vertices take longer to recognize. Interestingly, these same objects are nonetheless maintained in awareness for longer. The results suggest the mechanisms underlying object recognition are different from those subserving the maintenance of object awareness.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".