Object substitution masking of symbolic stimuli and the allocation of spatial attention.
Bibliographic record
Abstract
Symbolic cues, such as arrows or words, can affect the way we allocate attention in the visual field. In this project, we asked whether awareness of arrows and words would modulate this effect. In two experiments, we used object substitution masking to conceal arrows or words and measured their effect on the processing of spatial information. In the first experiment, participants performed a modified spatial Stroop task in which a word (ABOVE, BELOW, LEFT, or RIGHT) would appear inside a four-dot pattern at one of four locations above, below, left, or right of fixation. Participants had to identify the word, as quickly as possible, regardless of location. The four-dot pattern could offset simultaneously with the word (unmasked condition) or delayed by 200ms (masked condition). We found a typical compatibility effect between the word identity and location when the word reached awareness and a negative compatibility effect when the word was successfully masked. A second experiment, used similar procedures except that an arrow (pointing left or right) was presented within a four-dot pattern either above or below fixation. Participants made a speeded response to the onset of a target on the left or right of the screen. As before, the four-dot pattern could offset simultaneously with the arrow (unmasked) or 200ms later (masked). We found that compatible arrow cues facilitated target detection in the unmasked condition, but this pattern reversed in the masked condition where target detection was slower with compatible arrow cues. These findings indicate that the effect of symbolic cues depends on awareness, and that symbolic cues presented below awareness can produce negative compatibility effects. Meeting abstract presented at VSS 2014
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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 | 0.001 | 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 | 0.000 |
| 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".