Bibliographic record
Abstract
Backward masking is the reduction in the visibility of a shape (target) when followed closely by another pattern (mask). Recent research has focused on the modulation of masking by spatial attention and gestalt influences, suggesting that backward masking occurs as a natural consequence of the object formation and updating processes that occur whenever the visual system is confronted with rapidly changing input (Enns, Lleras & Moore, 2009). Here we study how backward masking of shape is influenced by a motion sequence, comprised of visible shapes that precede and follow the masked target. On each trial, the target (34 ms shape, 34 ms blank, 34 ms mask) was preceded and followed by visible shapes (102 ms). Critically, the first and last shapes combined with the masked target to form (a) a linear motion path, (b) a curved motion path, or (c) incoherent motion. Participants discriminated between three possible masked target shapes under three different levels of mask intensity. Visual sensitivity was strongly influenced by the motion sequence, with much greater visibility when the target shape was consistent with a linear motion path than with a curved or incoherent path. Increased mask intensity also reduced target visibility more strongly for curved and incoherent paths than for linear motion. More detailed analyses will quantify the unique influence of the preceding and subsequent context shapes on target visibility. This methodology is offered as a new way to study the influence of spatial-temporal context on shape perception. Experiments are underway to extend it to speeded action tasks involving either indirect responses (i.e., key presses) or direct manual actions to the objects in motion (i.e., finger pointing).
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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 | 0.002 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".