Neural substrates of socioemotional self‐awareness in neurodegenerative disease
Bibliographic record
Abstract
BACKGROUND: Neuroimaging studies examining neural substrates of impaired self-awareness in patients with neurodegenerative diseases have shown divergent results depending on the modality (cognitive, emotional, behavioral) of awareness. Evidence is accumulating to suggest that self-awareness arises from a combination of modality-specific and large-scale supramodal neural networks. METHODS: We investigated the structural substrates of patients' tendency to overestimate or underestimate their own capacity to demonstrate empathic concern for others. Subjects' level of empathic concern was measured using the Interpersonal Reactivity Index, and subject-informant discrepancy scores were used to predict regional atrophy pattern, using voxel-based morphometry analysis. Of the 102 subjects, 83 were patients with neurodegenerative diseases such as behavioral variant frontotemporal dementia (bvFTD) or semantic variant primary progressive aphasia (svPPA); the other 19 were healthy older adults. RESULTS: bvFTD and svPPA patients typically overestimated their level of empathic concern compared to controls, and overestimating one's empathic concern predicted damage to predominantly right-hemispheric anterior infero-lateral temporal regions, whereas underestimating one's empathic concern showed no neuroanatomical basis. CONCLUSIONS: These findings suggest that overestimation and underestimation of one's capacity for empathic concern cannot be interpreted as varying degrees of the same phenomenon, but may arise from different pathophysiological processes. Damage to anterior infero-lateral temporal regions has been associated with semantic self-knowledge, emotion processing, and social perspective taking; neuropsychological functions partly associated with empathic concern itself. These findings support the hypothesis that-at least in the socioemotional domain-neural substrates of self-awareness are partly modality-specific.
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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".