Attentional control settings are stored in activated long term memory
Bibliographic record
Abstract
Recent work in our lab has shown that participants can adopt an attentional control set (ACS) for 30 visual objects, indicating that the contents of ACSs are stored in long term memory (LTM). This finding raises a question: What is unique about ACS representations in LTM that allows them to influence attentional capture, when most LTM representations do not? One proposition is that ACS representations are stored with greater than normal baseline activation, a state referred to as activated LTM (ALTM). In the present study we evaluated this proposition by testing whether ACS items exhibit a signature of ALTM: an intrusion effect in a working memory change detection task. Specifically, if ACS representations are maintained in ALTM, participants should be slow to correctly reject these items when they appear as the probe on "change" trials during this task. For our study, participants memorized 30 images of everyday visual objects and then completed two tasks, randomly mixed across trials: spatial blink trials (to induce an ACS for the memorized objects and to test for contingent capture), and visual working memory trials (to test for an intrusion effect). Replicating our previous contingent capture findings, on spatial blink trials, ACS objects captured attention more than non-ACS objects. On working memory trials, ACS objects produced an intrusion effect and non-ACS objects did not. This pattern supports the conclusion that the contents of ACSs are maintained in ALTM. More broadly, the present findings add to the growing evidence that LTM has rapid attentional effects during perceptual processing, and that these effects are regulated through differential activation of LTM representations. Meeting abstract presented at VSS 2017
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".