Bibliographic record
Abstract
Visual attention and visual working memory (VWM) are intertwined processes that allow navigation of the visual world. These systems can compete for highly limited cognitive resources, creating interference effects when both operate in tandem. Previous research has shown that selectively attending, compared to non-selectively attending, an item causes obligatory interference with concurrently maintained VWM information. This finding may reflect that selectively attended items are automatically encoded into VWM. The current study examines this proposal by utilizing the procedures of the memory-driven capture paradigm. If an item is stored in VWM, then attention is captured by feature-matching items in subsequent tasks, and importantly even if they are distractors. On Trial 1, participants searched for a diamond shape with a varying colour. The target diamond was presented either alone (non-selectively attended condition) or among differently coloured non-targets (selectively attended). On Trial 2, the diamond-target and non-targets were the same colour, and one of the non-targets now had a singleton colour. This distractor colour could either match the colour of the diamond target from Trial 1 or was a novel colour. If a selectively attended item is automatically encoded into VWM, then it the feature-matching distractor capture on Trial 2 (measured via Eye-movements and RTs) should be higher for the selectively attended colour (Trial 1). This capture should also be above and beyond the effects of feature priming from the non-selectively attended Trial 1 colour. The results support this finding, the difference between matching and novel distractor colours are larger for the selectively attended condition compared to the non-selective attention condition. This study displays the effects of memory-driven capture in a task where participants were never required to encode stimuli into VWM and suggests that selective attention leads to obligatory VWM encoding.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".