Reduced distractor filtering with age: Evidence from the distractor positivity ERP
Bibliographic record
Abstract
In our everyday lives, there are many instances during which we must guide our attention towards a goal while ignoring irrelevant information. In these situations, we rely on attentional control to engage cognitive resources necessary to ignore salient irrelevant distractors. Although this process can be facilitated by providing cues (e.g., a positive cue that indicates attend to blue), negative cues (indicating what to ignore) may initially bias attention towards distractors (Zhang et al., 2020). This may be especially the case in individuals with less efficient inhibitory control, such as anxious individuals (Salahub & Emrich, 2021) and older adults (Torres et al., 2023; Weeks et al., 2020) . To test the efficacy of target and distractor processing in a sample with lower inhibitory abilities, older adults’ filtering performance was compared to that of younger adults during a search task while EEG was recorded. Participants were provided with either positive or negative pre-cues to indicate the feature of the target or distractor location, as well as a neutral control condition. The results indicate older adults only benefit from positive cues, as demonstrated by a higher mean amplitude of the N2pc component, as well as shorter reaction times, in response to lateral targets. However, in contrast to young adults, when presented with negative cues, older adults showed no Pd component to lateral distractors in any condition, suggesting that older adults did not inhibit the distracting information. These results suggest that older adults (with impaired inhibitory abilities) have particular difficulty suppressing distractors when a negative cue is provided, presumably because they have difficult disengaging attention from negatively cued items once it is directed there.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| 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".