Theta and alpha oscillations as signatures of internal and external attention to delayed intentions: A magnetoencephalography (MEG) study
Bibliographic record
Abstract
BACKGROUND: Remembering to execute delayed intentions (i.e., prospective memory, PM) entails the allocation of internal and external attention. These processes are crucial for rehearsing PM intentions in memory and for monitoring the presence of the PM cue in the environment, respectively. AIM: The study took advantage of the excellent spatial and temporal resolution of magnetoencephalography (MEG) to delineate the neural mechanisms of the memory and monitoring processes underlying PM. METHOD: The spatio-temporal dynamic of theta and alpha oscillations were explored in 21 participants in two PM tasks compared to a baseline condition (i.e., a lexical decision task with no PM instruction). The PM tasks varied for the load of internally-directed attention (Retrospective-load task) vs externally-directed attention (Monitoring-load task). RESULTS: Increase in theta activity was observed in the Retrospective-load task, and was particularly expressed in the regions of the Default Mode Network, such as in medial temporal regions, precuneus, posterior cingulate cortex and medial prefrontal cortex. Alpha decrease was the most relevant feature of the Monitoring-load task, and it was expressed over bilateral occipital, occipito-parietal and fronto-temporal regions, as well as over left dorsal fronto-parietal regions. CONCLUSIONS: Theta and alpha oscillations are strictly associated with the direction of attention during the PM tasks. In particular, theta increase is linked to internal attention necessary for maintaining the intention active in working memory, whereas alpha decrease supports the external attention for detecting the PM cue in the environment.
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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 | 0.001 |
| 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".