Identifying causal neural oscillations underlying working memory
Bibliographic record
Abstract
Electroencephalography is instrumental in understanding neurophysiological mechanisms underlying working memory. While numerous studies have associated electroencephalography features to working memory, understanding causal relationships leads to better characterization of the neurophysiological mechanisms that are directly linked to working memory. Personalized causal modeling is a tool to discover these direct links between brain features and working memory performance. Therefore, we applied this approach to electroencephalography data from 66 adult healthy participants collected while performing a 3-back working memory task. Using graphical causal modeling, we discovered causal neural oscillations of working memory performance and compared the causal features between two groups: high and low performers. Total number of causal features in high performers was higher than low performers. Among the causal features, right temporal gamma oscillation was ~5 times (z-score = 3.87, P = 0.0001) more frequently a causal feature among high performers than low performers. However, the power of causal temporal gamma oscillation was not different between the two groups. Our findings suggest that one potential approach to improve working memory performance is to induce more causal gamma oscillations. This can be achieved by generating more local gamma entrainment over the right temporal cortex, rather than simply increasing gamma power.
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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 | 0.001 | 0.006 |
| 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) | 0.002 | 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".