Object-substitution masking: The identity of the mask does matter!
Bibliographic record
Abstract
The current experiment extends a previous study completed by Olds and Weber (VSS '04), in which a traditional object-substitution masking (OSM) task was combined with a shape manipulation. The design closely resembles that of Enns and Di Lollo's (1997) experiment, in which 4 dots, presented around one of three modified-diamond targets (diamond shapes missing a corner either on the left side or on the right side), trail in the display after target offset; in the present study, the 4-“dot” mask consisted of 4 smaller modified-diamonds (all missing a corner on the left side, or all missing a corner on the right side). The delay between target onset and mask onset (stimulus-onset asynchrony, or SOA) was 80 ms. Our previous study found an effect of target-mask similarity: when the modified-diamond target, and the modified-diamonds of the mask, were the same shape, accuracy was much higher than when the target and mask were different shapes. The present study manipulated the proportion of trials with masks missing corners on the left, versus trials with masks missing corners on the right, to determine whether the effect of mask identity could be attributed to re-entrant processing or simply to a response bias. The present study showed that the effect of target-mask similarity (replicated here) was not attributable to response bias, for these 80-ms SOA trials. Furthermore, in addition to this 80-ms-SOA condition (traditional OSM), a common-onset condition (mask duration 300 ms; trials intermixed with OSM trials) allowed us to compare effects of re-entrant processing with effects of interruption by a transient mask. Performance was lower in the common-onset condition than in the 80-ms-SOA “OSM” condition; in addition, a response bias was evident in the common-onset condition, along with very poor performance when target and mask were different shapes (indicating that observers were responding to the mask rather than the target, on common-onset trials).
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".