An imagery-induced reversal of intertrial priming in visual search.
Bibliographic record
Abstract
Maljkovic and Nakayama (1994) found that pop-out search performance is more efficient when a singleton target feature repeats rather than switches from 1 trial to the next-an effect known as priming of pop-out (PoP). They also reported findings indicating that the PoP effect is strongly automatic, as it was unaffected by knowledge of the upcoming target color. In the present study, we examined the impact of visual imagery on the PoP effect. Participants were instructed to imagine a target color that was opposite that of the preceding trial (e.g., if the prior target was red, then imagine green). Under these conditions, responses were faster for targets that matched the imagined color than for targets that matched the previous target color, reversing the typical PoP effect. There was no such reversal of the PoP effect for participants asked to verbalize rather than imagine an upcoming target color. In Experiment 3, we explored whether the PoP effect was indeed eliminated in the prior experiments, or instead obscured by the opposing visual imagery effect. Two conditions were compared, 1 in which a PoP effect could oppose the visual imagery effect, and another in which no such effect was possible, allowing inferences about whether a PoP effect was present. The results indicated that the PoP effect was present, but obscured by the larger visual imagery strategy effect that pushed performance in the opposite direction. Overall, the results suggest that the PoP effect is sensitive to top-down strategies that involve visual representations. (PsycINFO Database Record
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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 | 0.006 |
| 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 | 0.001 |
| 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".