EEG Patterns and Performance When Facing the Cardinal Directions
Bibliographic record
Abstract
1) Background and Objectives: Position in space and passage of time are encoded in the firing of thalamic, hippocampal and entorhinal cortices in rodents. Head direction cells have been reported in freely moving monkeys, and differential brain patterns have been observed in humans while playing a navigation video game and in response to changes in electromagnetic fields. The sensitivity of organisms to environmental and electromagnetic cues could explain recommendations from a traditional system of architecture, Vastu architecture, which recommends aligning homes to the cardinal directions. 2) Hypothesis: Vastu architecture predicts that facing east and north are more advantageous than facing west and south. If facing east and north are more advantageous, then subjects should show distinct EEG patterns and improved performance when facing east and north compared to west or south. 3) Materials and Methods: EEG coherence patterns from 32-channel EEG and time-to-complete jigsaw puzzles were compared while subjects faced the four cardinal directions. 4) Results: When facing east and north, subjects’ frontal beta2 and gamma EEG coherence were significantly higher, and they assembled jigsaw puzzles significantly faster than when facing west or south. 5) Discussion: The brain findings fit the performance data. Better focus, which would reasonably be related with faster performance, is associated with higher levels of beta2 and gamma coherence. 6) Conclusion: These data support the possibility that the human brain may be sensitive to cardinal directions. This highlights how intimately we are connected to the environment and suggests a factor that may be important in orienting work spaces and designing class rooms.
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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) | 0.003 | 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".