Tracking the Time-Course of Attentional Sharpening using EEG
Bibliographic record
Abstract
Attentional templates are optimally tuned according to the expected similarity between targets and non-targets. However, our current understanding of the underlying mechanisms that support such tuning is limited. We thus used EEG to track encoding, maintenance and target selection processes while individuals expected to encounter either low or high target-distractor similarity. Participants were asked to memorize low-contrast sinusoidal gratings that varied across twelve possible orientations. Following a delay, they then had to discriminate the target from a lure that differed from the target by ±45° (i.e., coarse discrimination) or ±22.5° (fine discrimination). Across experimental sessions, the proportion of coarse discrimination trials versus fine discrimination trials was varied; in a Mostly Coarse session, coarse discrimination trials outnumbered fine discrimination trials by 3:1, whereas in a Mostly Fine session, the reverse proportion was true. As optimal tuning accounts would predict, accuracy was greater for the Mostly Fine session relative to the Mostly Coarse session, with this difference being largest for fine discrimination trials (indicative of attentional sharpening). Underlying this effect, we observed an attentional difference at encoding and maintenance, marked by greater suppression of posterior-alpha when fine-grained target-distractor discriminations were expected. This attentional difference may support posterior maintenance, as stimulus-specific activity was recoverable from raw EEG at occipital sites over this period if fine-grained discriminations were expected, but not if coarse-grained discriminations were expected. Lastly, at target presentation, we observed a between-session difference in the amplitude of a sustained posterior contralateral negativity ERP component, likely reflecting greater efficiency in template-match evaluations when fine-grained discriminations were expected over coarse-grained discriminations. Overall, we demonstrate that the expectation of high target-distractor similarity produces a global difference in attention at encoding and maintenance, alters the manner by which target representations are encoded and held, and allows for more efficient target-match decisions at the time of response.
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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 | 0.000 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".