Bibliographic record
Abstract
Relatively little is known about the role of brain oscillations in relation to cognitive function. While oscillations of all frequencies have be associated with most any neural process, no conclusive data has been found to support if oscillations are simply emergent or if they play a causal role in cognitive functions. To make headway on this problem, we employed entrainment, a technique used to synchronize brain oscillations. Entrainment was achieved by presenting subjects with alternating images of a neutral face and a scrambled face at 4 Hz such that the faces were presented at 2 Hz. After a few seconds of entrainment, a target image of either a face expressing happiness or disgust, or another scramble, was shown in-phase or out-of-phase of the entraining faces and followed by a masking image. Subjects were asked to identify if the target image was a scramble or a face, and if the face was expressing happiness or disgust. By monitoring neural activity with electroencephalography (EEG), we found that entrainment was successful. Oscillations in the occipital cortex were strongest around 4 Hz, and those in the parietal, central, and frontal cortices were strongest around 2 Hz. We also found that faces shown in-phase were easier to detect than those shown out-of-phase. Thus, initial results suggest entrainment can influence facial 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 | 0.001 |
| 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) | 0.002 | 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".