Transfer of Attentional Sharpening Across Contexts is Stimulus-Specific
Bibliographic record
Abstract
When targets frequently co-occur with highly similar distractors, attentional sharpening is observed wherein target feature representations are narrowed to aid target-distractor discriminations. This sharpening might be due to local tuning mechanisms through increased activation of target values along with suppression of learned distractor values (i.e., sharpening limited to learned features). Conversely, adjustments in global control settings may aid in conflict resolution more generally (i.e., sharpening is transferable to unlearned features). To contrast these models, we asked participants to locate and respond to color targets that co-occurred with two colored distractors (either from an intermediate distance +/-60º or near distance +/-30º from the target in hue space). Additionally, participants were assigned to either Mostly Intermediate or Mostly Near groups where the proportion of intermediate displays to near displays was 80:20 or 20:80, respectively, during a training phase. Following training, the ratio of intermediate to near displays was set to 50:50. In this transfer phase, the target and distractor colors either remained the same (Experiment 1) or changed (Experiment 2). In line with attentional sharpening, during the training phase distractors near the target color in hue space were less interfering for the Mostly Near group than for the Mostly Intermediate group. Critically, this attentional sharpening persisted in the transfer phase only when the target color remained constant. Thus, because the transfer of attentional sharpening was limited to trained features, our results indicate that this process occurs through local tuning processes rather than through broader attentional control mechanisms.
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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) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".