Attentional capture by items that match episodic long-term memory representations
Bibliographic record
Abstract
A remarkable ability of the human visual system is the implementation of attentional control settings (ACSs) that govern what stimuli capture or hold attention. We provide evidence that ACSs can be specified by episodic long-term memory representations. In all experiments, participants memorized 30 images of objects that they then monitored for in an attention task, inducing an episodic-based ACS. In Experiments 1a and 1b, only studied cues in a cueing task captured attention. We confirmed these cueing effects reflect capture by testing for inhibition of return in Experiment 2a, and controlled for perceptual masking by cues in Experiment 2b. In Experiment 3 we determined that ACSs are specifically supported by episodic retrieval, by dividing studied images into two sets and designating one as the targets in a rapid serial visual presentation task: Only target-set matching distractors produced a spatial blink (captured attention). These results extend our understanding of the representations specifying ACSs.
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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 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".