Does task complexity impact the neurovascular coupling response similarly between males and females?
Bibliographic record
Abstract
BACKGROUND: While previous studies have demonstrated a complex visual scene search elicits a robust neurovascular coupling (NVC) response, it is unknown how the duration of visual stimuli presentation influences NVC metrics. This study examined how stimuli duration, in addition to biological sex and self-reported engagement impact NVC responses. METHODS: Participants (n = 20, female = 10) completed four visual paradigms. Three involved simple visual shapes presented at 0.5-, 2-, and 4-s intervals in randomized orders. The fourth paradigm was a complex visual scene search ("Where's Waldo?"). Participants completed eight cycles of 20-s eyes-closed followed by 40-s eyes-open. Transcranial Doppler ultrasound indexed posterior and middle cerebral artery velocities (PCA and MCA). Participants self-reported their engagement following each task (1 [minimal] to 10 [maximal]). RESULTS: The "Where's Waldo?" task evoked greater PCA percent increase (all p < 0.001) and area under the curve during the first 30-s of the task (all p < 0.001) compared to simple shapes. Females displayed greater absolute baseline and peak PCA and MCA velocities across all tasks (all p < 0.002). Subjective engagement displayed moderate correlation levels with PCA percent increase (Spearman ρ = 0.58) and area under the curve (Spearman ρ = 0.60) metrics in males, whereas these were weak for females (Spearman ρ = 0.43 and ρ = 0.38, respectively). CONCLUSIONS: The complex visual paradigm "Where's Waldo?" greatly augmented the signal-to-noise ratio within the PCA aspects of the NVC response compared to simple shapes. While both sexes had similar NVC responses, task engagement was more related to NVC metrics in males compared to females. Therefore, future NVC investigations should consider task engagement when designing studies.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".