Early and late modulation of attentional selection by multiple attentional control sets: ERP evidence
Bibliographic record
Abstract
We have previously demonstrated that two attentional control sets (ACSs), each defined by a separate color, can be maintained over distinct locations in space, as observed in reaction times (RTs) to target stimuli. The current study used event-related potentials (ERP) to examine the neural responses to both cues and targets in order to determine the timing and specificity of attentional capture in this paradigm. Participants were instructed to maintain central fixation while responding to targets of only one color per location (e.g., blue target at left placeholder, green target at right placeholder). Prior to target onset, each placeholder was highlighted with a cue that matched the ACS for that side (“good” cue), a cue that matched the ACS for the opposite side (“bad” cue), or an achromatic cue (“neutral” cue). Behavioural results confirmed that target RTs are fastest for trials with good cues, relative both to trials with bad cues and to trials with neutral cues. This behavioural effect was reflected in the ERPs time-locked to target onset; specifically, the P3 component, a neural marker of attentional selection and consolidation, peaked at earlier latencies for targets following good cues. We also examined the ERPs time-locked to cue onset, specifically the N2pc component that reflects the current locus of attention. In our paradigm, a greater N2pc amplitude would indicate that attention has shifted in response to the cue. We found the greatest N2pc amplitudes for good cues relative to neutral cues, with a smaller although still discernible degree of capture for bad cues. Together these results suggest that late attentional selection is responsible for speeded target processing, and that ACSs do not operate as an early, all-or-nothing filter of bottom-up capture.
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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 | 0.001 | 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".