The Effects of Focal and Nonfocal Cues on the Neural Correlates of Prospective Memory: Insights From ERPs
Bibliographic record
Abstract
The present study is the first designed to investigate behavioral and event-related potential (ERP) correlates of the processes involved in focal and nonfocal prospective memory (PM) tasks. Focal tasks are those in which the features of the PM cue are easily extracted from the ongoing activity, whereas the process is more indirect in nonfocal tasks. Strategic monitoring was associated with a slowing of reaction times in ongoing trials and with frontal and parietal ERP modulations. These effects were greater in the nonfocal task, whereas they were smaller, or even absent for some individuals, in the focal task. This indicates that strategic monitoring is engaged to a greater extent in nonfocal tasks, whereas it is less extensively recruited, or not recruited at all by some individuals, in focal tasks. Indeed, the recognition of the PM cue might also occur automatically in focal tasks, as suggested by the FN400 increase in focal PM trials. Nonfocal tasks are supported by more controlled resources not only in retrieval, but also in postretrieval monitoring and coordinating processes. This was reflected in the enhancement of the prospective positivity and frontal slow wave observed in nonfocal PM trials. We interpreted these results as supporting the multiprocess view of PM.
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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 | 0.003 |
| 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 | 0.001 |
| 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".