Bibliographic record
Abstract
Can attentional goals spontaneously align to an environment through cyclical interactions between attention and visual working memory (VWM)? Representations in VWM can serve as attentional goals that modulate how stimuli capture attention; in turn, stimuli that capture attention are more likely to be encoded in VWM. Might such interactions allow new attentional goals to be adopted based on the relationship between past goals and the stimuli currently in the environment? Here, on every trial we had participants remember a shape and then complete two visual searches. In the first search, one of the distractor locations contained a shape singleton that was either a match or non-match with the shape in memory, and all items were heterogeneously coloured. The shape singleton should more strongly capture attention on match trials. Does this attentional bias cause the singleton to be encoded in memory, allowing its randomly chosen colour to serve as a new attentional goal? To assess this possibility, in search two all search items were circles, and one distractor was a colour singleton that either matched the colour of the search-one shape singleton or not. As is typically found, in search one we observed longer search times when the shape singleton matched the shape in memory, suggesting that memory biased attentional capture towards matching stimuli. We also found that, on these search-one matching trials, search-two reaction times were slower when the colour singleton matched the colour of the search-one shape singleton; no such difference was found on search-one non-matching trials. Thus, the stimulus that most strongly captured attention on search two was determined by physical properties of the stimulus that captured attention on search one. These findings are consistent with spontaneous updating of attentional goals following cyclical interactions between working memory, attention, and the environment.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".