The specificity of feature-based attentional guidance is equivalent under single- and dual-target search.
Bibliographic record
Abstract
Individuals actively maintain attentional templates to prioritize target-matching inputs. While previous works have established that multiple templates can be held simultaneously, current understanding is limited with respect to the representational quality of such templates. We thus investigated: (a) whether the maintenance of two templates is limited to broad, coarse-grained representations, and if not, (b) whether there is nonetheless a decline in the achievable level of specificity when multiple attentional templates are held simultaneously. Using a spatial cueing procedure, we probed the breadth of attentional templates while participants maintained either one (Experiment 1) or two target colors (Experiment 2) under conditions of low- or high-similarity search and found specific template maintenance during high-similarity search for both single- and dual-target conditions. We then directly compared template specificity during single- and dual-target maintenance in Experiment 3, probing at the point of differentiation between target and nontarget feature values observed during single-target search. Here we found no difference in the selectivity of cue validity effects between single- and dual-target search, suggesting equivalent template specificity regardless of whether one or two features are relevant to search. Lastly, in Experiment 4, we established that such template specificity is dependent on access to visual working memory. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
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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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".