Cortical Stimulation Associated with Tabletop Cognitive Activities and the Influence of Self-Perceived Challenge as Measured by Electroencephalography: A Pilot Study
Bibliographic record
Abstract
Background: Positive associations are reported between "cognitively stimulating" activities and cortical stimulation and between "cognitively challenging" activities and cortical stimulation. However, the basis for these has been largely subjective. One aim of this pilot study was to determine whether tabletop cognitive activities, believed to be cortically stimulating, are objectively based on left dorsolateral prefrontal cortex (LDLPFC) coherence measured by electroencephalography (EEG). A second aim was to compare LDLPFC coherence associated with Sudoku, perceived by most study participants to be the most cognitively challenging activity they completed, with LDLPFC coherence associated with the activity each perceived to be the least cognitively challenging. Methods: Participants engaged for five minutes in an "at rest" condition and each of five presumptively "cognitively stimulating" tabletop activity conditions. EEG data were collected throughout. Participants then ranked the cognitive challenge they experienced completing each tabletop activity. Results: Based on EEG LDLPFC coherence, not all activities were cortically stimulating. Sudoku, the activity rated "most cognitively challenging" by most participants (n = 13/25), was the most cortically stimulating condition in Beta, High Beta, Theta, Delta, and High Delta frequency bands. Conclusion: With a growing body of evidence supporting the benefits of ongoing engagement in challenging cognitive exercise for individuals across their lifespans, identifying cognitive activities that stimulate beneficial cortical activation and, ultimately, cognitive function is needed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.003 | 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".