Cognitive correlates of deception recognition in the elderly and seniors
Bibliographic record
Abstract
The relevance of the proposed study is due to the need to find solutions in reducing the vulnerability of elderly and seniors to deception and fraudulent actions. The purpose of the study is to assess the cognitive correlates of deception recognition in the elderly and seniors. The sample size was 87 elderly and senile subjects (60–89 years old) — 38 men and 49 women. Research methods: MoCA (Montreal Cognitive Assessment); Sally–Anne test; Pragmatic intervention short stories Winner's Task; experimental method Read the Mind in the eye (RMET); Dembo–Rubinstein self-esteem scale; trust self-esteem scale. Based on the findings of the study, the cognitive correlates of deception recognition in the elderly and seniors were identified. It is reliably found that with age, as ageing progresses regardless of education level, there is a decline in cognitive level, which, in general, is natural in the process of normative ageing. These changes lead to a decrease in the level of understanding of the mental model, which in turn makes it more difficult to recognise emotions and increase trust. The empirical study supported the hypothesis that there is a correlation between cognitive level and the ability to recognise deception. The lower the general cognitive level, the worse the deception is recognised and the more trusting a person becomes.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".