Contextual distinctiveness produces long-lasting priming of pop-out.
Bibliographic record
Abstract
Maljkovic and Nakayama have demonstrated memory influences in singleton search from one trial to the next, an effect they termed priming of pop-out (PoP). This effect was described as resulting from the persistence of an implicit memory trace, the influence of which could be observed for around 5-8 subsequent trials. Thomson and Milliken (2012) recently reported that PoP effects can survive a lag of up to 16 trials for "rare" trials that were composed of distinct target and distracter colors relative to intervening "common" trials. The present experiments tested the idea that long-term PoP effects can depend on the retrieval of distinct contextual cues. Across the experiments reported here, rare trials differed from common trials in spatial location (Experiments 1A and 2A), stimulus configuration (Experiments 1B and 2B), target and distracter colors (Experiments 2A and 2B), or response-selection task (Experiment 3). PoP effects that survived 15 intervening trials were observed with rare search stimuli that were composed of distinct target and distracter colors or required a distinct selection task. Distinct stimulus location and distinct stimulus configuration failed to produce a measurable effect on PoP for rare trials, either on their own or in conjunction with other distinct features. These results are interpreted as evidence that episodic memory retrieval processes can produce relatively long-term PoP effects.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
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".