Deficits in the knowledge of social norms and their underlying mechanisms in Alzheimer’s disease
Bibliographic record
Abstract
Abstract Background Compared to other components of social cognition, knowledge of social norms has received less attention, even more so in Alzheimer’s disease (AD). While semantic memory deficits have been identified early in the course of AD, no study has delved into the knowledge of social norms at these preliminary stages, although evidence suggests it shares common ground with semantic memory. In addition, it is unclear whether the knowledge of social norms in AD is associated socioemotional deficits, as seen in the behavioral variant of frontotemporal dementia (bvFTD). Finally, how social norms knowledge impairments predict behaviours in real-world settings remains unknown in the context of AD. Methods This study included 286 participants with mild cognitive impairment (MCI), 157 with AD, 285 with bvFTD along with 384 older healthy controls. All participants were selected from the National Alzheimer’s Coordinating Center. They completed the Social Norms Questionnaire, which assesses the tendency to break or overadhere to social norms. They also completed tests assessing executive, semantic and socioemotional functions, along with tests measuring spontaneous interpersonal behaviours. Results Between-group comparisons show that individuals with AD and MCI break and overadhere to social norms significantly more than HC, while they perform better than individuals with bvFTD. Knowledge of social norms was mainly associated with semantic knowledge across groups, and predicted insensitivity and disinhibition severity in patients. Conclusions This study suggests that declines in semantic memory likely play a key role in social norms knowledge decreases and that these decreases predict behavioural tendencies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".