Distinct and complementary mechanisms of oscillatory and aperiodic alpha activity in visuospatial attention
Bibliographic record
Abstract
Alpha oscillations are thought to play a key role in visuospatial attention, particularly through lateralisation mechanisms. However, whether this function is driven purely by oscillatory activity or also involves aperiodic neural components remains unclear, making it difficult to develop precise theoretical models of alpha function and attention. Using EEG and concurrent transcranial magnetic stimulation (TMS)-EEG, this study aimed to (1) disentangle the contributions of oscillatory and aperiodic alpha activity to visuospatial attention and (2) examine their causal roles by differentially modulating aperiodic and oscillatory components. First, across four independent EEG datasets, we found that both aperiodic and oscillatory alpha activity contribute to spatial attention encoding and univariate lateralisation effects. The two signals were uncorrelated across electrodes and their combination yielded stronger lateralisation effects than either signal separately, suggesting that they may play complementary roles. Then, we used concurrent TMS-EEG to modulate the two signals. Compared with arrhythmic TMS (ar-TMS), rhythmic TMS (rh-TMS) enhanced oscillatory alpha power, especially at the stimulated area, while decreasing aperiodic alpha power across the scalp. Despite these opposing effects, rh-TMS improved visuospatial attention representation carried by both oscillatory and aperiodic alpha signals, suggesting that both signals may reflect attentional processing. Moreover, TMS-induced changes in oscillatory and aperiodic alpha decoding differentially predicted behavioural performance, with TMS-induced changes in oscillatory alpha decoding correlating with response errors and changes in aperiodic alpha decoding correlating with response speed. Together these findings reveal a functional dissociation between oscillatory and aperiodic activity in the alpha band. We suggest a dual mechanism for alpha band activity in supporting visuospatial attention, where the two components have distinct but complementary roles. Oscillatory components may primarily support attentional filtering and target prioritisation, while aperiodic components may reflect overall neural excitability and cognitive efficiency. Both of these mechanisms contribute to successful visuospatial attention.
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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 | 0.001 | 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) | 0.001 | 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".