Goal-directed attention in late visual processing: On the scope and flexibility of feature-based attention
Bibliographic record
Abstract
Feature-based attention (FBA) has known effects on visual perception; however, its effects on later processing (i.e., visual working memory; VWM) are not well understood. Across three experiments I used a partial-report task to assess effects of FBA on the probability that items were encoded into VWM, and the precision of their representations. To investigate the flexibility of feature-based control I manipulated the likelihood that the feature-based goal would be relevant. Experiments 1 and 2 defined the relevant feature prior to encoding, and, to isolate later functioning, Experiment 3 defined the relevant feature per trial following encoding. Experiments 1 and 2 revealed that FBA affects both VWM probability and resolution, and these effects operate independently and flexibly. Experiment 3 revealed a later, less flexible mechanism for FBA. FBA has multiple effects on VWM that are separately influenced by the value of the goal, and the timing of when it is implemented.
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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 | 0.004 |
| 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) | 0.001 | 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".