The effect of movie-watching on electroencephalographic responses to tactile stimulation
Bibliographic record
Abstract
Movie-watching is becoming a popular acquisition method to increase compliance and enable neuroimaging data collection in challenging populations such as children, with potential to facilitate studying the somatosensory system. However, relatively little is known about the possible crossmodal (audiovisual) influence of movies on cortical somatosensory processing. In this study, we examined the impact of dynamic audiovisual movies on concurrent cortical somatosensory processing using electroencephalography (EEG). Forty healthy young adults (18-25 years) received passive tactile fingertip stimulation while watching an "entertaining" movie and a novel "low-demand" movie called 'Inscapes' compared to eyes-open rest. Watching a movie did not modulate properties of early or late somatosensory-evoked potentials (SEPs). Similarly, no crossmodal influence on somatosensory adaptation, denoted by a reduction in SEP amplitude with repetitive tactile stimulation, was found. The prominent oscillatory responses in the alpha and beta frequency bands following tactile stimulation differed as a function of viewing condition, with stronger alpha/beta event-related desynchronization (ERD) during movie-watching compared to rest. These findings highlight that movie-watching is a valid acquisition method during which SEPs can be measured in basic research and clinical studies, but that the attentional demands of movies need to be taken into account when performing oscillatory analyses.
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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) | 0.001 | 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".