Mask-target color congruency enhances object substitution masking in the presence of an attentional control set
Bibliographic record
Abstract
In the object substitution masking (OSM) phenomenon, briefly shown targets can be masked by presenting four small dots that surround the targets but do not touch it (Enns & Di Lollo, 1997). The purpose of the present study was to determine if masking stimuli that are present and visible, but not attended to, can generate OSM. In other words, must the masking stimuli be attended to in order for OSM to occur? To accomplish this, we used top-down attentional control settings, which have profound effects on what sort of stimuli capture attention; stimuli that have features that match a target capture attention while stimuli that mismatch with target features are ignored. In our experiment, subjects were told to locate a target (distinguished by color from distractors) and to identify a feature on the target, thus creating an attentional control set for that color. Four-dot masks with either the same (mask-target match) or different color (mask-target mismatch) as the target were presented at SOAs ranging from -144 to 144 ms. When subjects searched for green targets, masking was enhanced if the four-dot mask was also green (match) as opposed to red (mismatch). This difference was largest 48 ms after target offset. Also, there was no difference between color match and mismatch masks when there was no attentional control set for green. Thus, the present results cannot be attributed simply to mask-target color congruency effects but rather the interplay of attention and vision. Specifically, our results show that masking is considerably enhanced when the four-dot mask falls within the subject’s attentional control set. Overall, this suggests that object substitution masking is mediated partly by attention and subject to top-down attention control. Meeting abstract presented at VSS 2012
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.
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) | 0.003 | 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".