Reduced distractor filtering with age: Evidence from the distractor positivity ERP
Bibliographic record
Abstract
Previous behavioral research has demonstrated that when given positive and negative cues (e.g., attend to blue vs ignore red), young and older adults are able to use this information to a similar extent. However, it is possible that older adults achieve similar behavioral performance via different cognitive and neural mechanisms. The current study aimed to test this question by examining the neural underpinnings of attentional filtering with age. Young and older adults were presented with either positive (target matching), negative (distractor matching), or neutral cues, which were immediately followed by a search array in which participants had to report the orientation of a search target. We found that both age groups appropriately attended to target information when given a target-matching pre-cue, as indicated by faster response times (RTs) and a significant N2pc event-related potential (ERP) related to increased attentional selection. However, only young adults showed suppression of distractors, as indicated by a significant distractor-positivity (PD) ERP following all three cue types. Older adults did not show significant suppression of distractors in any condition and, they even showed increased attention towards distractors following negative and neutral cues. Thus, although behavioral evidence suggests young and older adults seem to use negative cues similarly, neural evidence suggests that older adults are less able to suppress distractors following these cues.
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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 | 0.001 | 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) | 0.003 | 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".