The Impact of Television Viewing on Brain Structures: Cross-Sectional and Longitudinal Analyses
Bibliographic record
Abstract
Television (TV) viewing is known to affect children's verbal abilities and other physical, cognitive, and emotional development in psychological studies. However, the brain structural development associated with TV viewing has never been investigated. Here we examined cross-sectional correlations between the duration of TV viewing and regional gray/white matter volume (rGMV/rWMV) among 133 boys and 143 girls as well as correlations between the duration of TV viewing and longitudinal changes that occurred a few years later among 111 boys and 105 girls. After correcting for confounding factors, we found positive effects of TV viewing on rGMV of the frontopolar and medial prefrontal areas in cross-sectional and longitudinal analyses, positive effects of TV viewing on rGMV/rWMV of areas of the visual cortex in cross-sectional analyses, and positive effects of TV viewing on rGMV of the hypothalamus/septum and sensorimotor areas in longitudinal analyses. We also confirmed negative effects of TV viewing on verbal intelligence quotient (IQ) in cross-sectional and longitudinal analyses. These anatomical correlates may be linked to previously known effects of TV viewing on verbal competence, aggression, and physical activity. In particular, the present results showed effects of TV viewing on the frontopolar area of the brain, which has been associated with intellectual abilities.
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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 | 0.002 |
| 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 | 0.000 |
| 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".