Connecting Covert Attention and Visual Perception to the Spatiotemporal Dynamics of Alpha Band Activity, Cross-Frequency Coupling (CFC), and Functional Connectivity using Multivariate Pattern Analysis (MVPA)
Bibliographic record
Abstract
Abstract Some evidence suggests that alpha activity is directly related to the baseline firing rate of sensory neurons which alters the probability of responding to a stimulus. Other evidence points to alpha indirectly modulating cortical excitability through its interactions with gamma oscillations. A third possibility is that alpha-based functional connectivity better explains attentional modulation and perceptual responses. To test this, alpha amplitude, CFC, and functional connectivity measures were extracted from EEG data recorded while participants performed a cued orientation perception task. Using pre-target data, the spatiotemporal activity of each metric was submitted to a SVM classifier to determine which activity pattern best distinguished trials with covert attention from trials without. The same metrics were submitted to SVR to find the activity that best predicted task performance. Results indicate the best metric for classifying trials with and without covert attention was alpha amplitude. This indicates that, prior to target onset, alpha amplitude alone is most sensitive to the presence of covert attention. In contrast, none of the metrics were strong predictors of task performance. Overall, our results support the idea that alpha activity is directly related to changes in the baseline firing rate of sensory neurons which changes responsiveness but not performance.
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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 | 0.003 |
| 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 | 0.001 | 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".