From neural activity to behavioral engagement: temporal dynamics as their “common currency” during music
Bibliographic record
Abstract
The human cortex is highly dynamic as manifest in its vast ongoing temporal repertoire. Similarly, human behavior is also variable over time with, for instance, fluctuating response times. How the brain's ongoing dynamics relates to the fluctuating dynamics of behavior such as emotions remains yet unclear, though. We measure median frequency (MF) in a dynamic way (D-MF) to investigate the dynamics in both electroencephalography (EEG) neural activity and the subjects' continuous behavioral assessment of their perceived emotional engagement changes during five different music pieces. Our main findings are: (i) significant differences in the frequency dynamics, e.g., D-MF, of the subjects' behavioral engagement ratings between the five music pieces, (ii) significant differences in the, e.g., D-MF, of the music pieces' EEG-based neural activity, and (iii) there is a unidirectional relationship from neural to behavioral during the five music pieces as measured through correlation and Granger causality between their respective D-MF's. Together, we demonstrate that neural dynamics relates to behavioral dynamics through the shared fluctuations in their dynamics. This highlights the key role of dynamics in connecting neural and behavioral activity as their "common currency."
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".