Distinct Alpha Networks Modulate Different Aspects of Perceptual Decision-Making
Bibliographic record
Abstract
SUMMARY Why do we sometimes perceive a faint stimulus but miss it at other times? One explanation is that conscious perception fluctuates with the brain’s internal state, influencing how external stimuli are processed. Ongoing brain oscillations in the alpha band (8–13 Hz), thought to reflect neuronal excitability levels 1–5 and play a role in functional inhibition 6,7 , have been shown as a key contributor to such perceptual variability 8,9 . Under high alpha conditions, faint stimuli are more likely to be missed 8 . Some studies suggested alpha oscillations modulate perceptual criterion ( c ) 10–14 , shifting the threshold for interpreting sensory information; while others (including our prior work 15 ) suggested alpha modulates sensitivity ( d’ ) 15–19 , changing the precision of sensory encoding. Few studies observed modulations in both metrics, making these results appear mutually exclusive. Most studies have focused solely on overall alpha activity— whether within a region of interest or across the whole brain—and overlooked the coexistence of multiple distinct alpha networks 20–26 , which fluctuate in terms of predominance 20,27,28 and adapt to behavioural demands 29,30 . Hence, it remained unclear whether different networks’ contributions to perception vary with their momentary state. Here, aiming to characterize how different alpha networks influence perceptual decision-making, we analyzed magnetoencephalography (MEG) data recorded while participants performed a visual detection task with threshold-level stimuli. We found that while the visual alpha network modulates perceptual sensitivity, the sensorimotor alpha network modulates criterion in perceptual decision-making. These findings reconcile previous conflicting results and highlight the functional diversity of alpha networks in shaping perception.
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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 | 0.000 | 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".