Replace Psychometric Inferences with Direct Brain Measurements: LORETA Reflects Traditional Cerebral Loci for Neuropsychological Tests
Bibliographic record
Abstract
Inferences of subtle cerebral injury and dysfunction have been historically dependent upon psychometric tests from which clinical neuropsychological profiles are generated. In addition to being secondary, over-inclusive and crude indicators of cerebral activity, psychometric tests are subject to economic incentives to “re-norm” traditional methods under the pretense of “ensuring” contemporary representations that are sanctioned by regulating organizations dominated by agendas of control over the interpretations of clinicians. The validity of neuropsychological tests is essential for their perspicacious application and interpretations. We measured the quantitative electroen-cephalographic profiles and calculated s-LORETA (standardized Low Resolution Electromagnetic Tomography) profiles in real time for normal men and women while they engaged in both traditional and novel neuropsychological tests that were employed to infer localized brain injury. Conspicuous alterations in source current density within specific frequency bands occurred within various regions of the right prefrontal region during performance of the Category, Design Fluency and Conditioned Spatial Association Test, the prefrontal medial surface during Toe Graphaesthesia, the caudal medial surface during Toe Gnosis, the left temporal region during Speech-Sounds, and within the right retrosplenial-parahippocampal region for Seashore Rhythms. Results supported the well established regional associations with the classic neuropsychological tests, verified the cerebral localization with more recent procedures, and emphasized the utility of modern real-time, direct cerebral imaging procedures.
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 | 0.015 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".