Bridging the gap – Spontaneous fluctuations shape stimulus-evoked spectral power
Bibliographic record
Abstract
Abstract Spontaneous fluctuations of neural activity have been shown to influence trial-by-trial variation in perceptual, cognitive, and behavioural outcomes. This implies that these fluctuations affect stimulus-related neural processes, and hence should affect stimulus-evoked neural activity. However, the mechanisms by which spontaneous neural activity shapes stimulus-evoked neural activity have rarely been examined. Employing a large-scale magnetoencephalographic dataset, as well as an electroencephalographic replication dataset, we observed that for high-frequency power, high pre-stimulus activity leads to greater evoked desynchronization (negative interaction); in contrast, for low-frequency power, high pre-stimulus activity induces greater event-related synchronization (positive interaction). We show that both positive and negative interactions are manifest primarily in cortical oscillations, rather than scale-free activity, and can also be observed in the time domain. In summary, we demonstrate positive and negative spontaneous-evoked interaction in multiple electrophysiological processes; these mechanisms “bridge the gap” between spontaneous and evoked activity and provide novel insights into how spontaneous activity influences behaviour and cognition.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".