Brain laterality, depression and anxiety disorders: New findings for emotional and verbal dichotic listening in individuals at risk for depression
Bibliographic record
Abstract
Studies using dichotic listening tests and electroencephalographic (EEG) measures of hemispheric asymmetry have reported evidence of abnormal brain laterality in patients having depressive disorders. We present new findings from a multigenerational study of risk for depression, in which perceptual asymmetry was measured in dichotic listening tests of emotional and verbal processing. Biological offspring and grandchildren of probands with a major depressive disorder (MDD) who were at high risk and those of nondepressed controls who were at low risk were tested on dichotic emotional recognition and consonant-vowel syllable tests. In the emotion test, individuals with a lifetime diagnosis of MDD had a smaller right hemisphere advantage than those without a MDD, but there was no difference between high- and low-risk groups or between those with or without an anxiety disorder. In the syllable test, a smaller left hemisphere advantage was found in individuals with an anxiety disorder compared to those without an anxiety disorder, but there was no difference between high- and low-risk groups or between those with or without a MDD. This double dissociation indicates that lifetime diagnosis of MDD and anxiety disorders have a differential impact on lateralized hemispheric processing of emotional and verbal information.
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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 | 0.001 | 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".