Long-term memory representations influence perception before edges are assigned to objects.
Bibliographic record
Abstract
One way to prioritize limited mental resources for perception is to take into account the familiarity of an object to the perceiver. But does an objects' familiarity influence perception only after an object's shape has been determined, or does it influence the decision of which edges are considered part of that object? Here we compare the influence of target familiarity on whole-object masking (object-substitution masking) with its influence on edge-based masking. Two new aspects of edge-based masking are reported. First, we demonstrate that mask and target edges do not only compete (object trimming) but that mask and target edges can also cooperate (object binding), confirming that these masking effects are indeed occurring during the process of object formation and not after object shape has been determined. Second, we find that object trimming and binding are each less likely if the target is linked with a representation already present in long-term memory. Since trimming and binding effects arise very early in visual perception, these data indicate that existing long term memory representations influence the earliest stages of object assembly, before the system has even decided which edges to include in the object.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".