Motivation and social-cognitive abilities in older adults: Convergent evidence from self-report measures and cardiovascular reactivity
Bibliographic record
Abstract
Recently, some authors have suggested that age-related impairments in social-cognitive abilities-emotion recognition (ER) and theory of mind (ToM)-may be explained in terms of reduced motivation and effort mobilization in older adults. We examined performance on ER and ToM tasks, as well as corresponding control tasks, experimentally manipulating self-involvement. Sixty-one older adults and 57 young adults were randomly assigned to either a High or Low self-involvement condition. In the first condition, self-involvement was raised by telling participants were told that good task performance was associated with a number of positive, personally relevant social outcomes. Motivation was measured with both subjective (self-report questionnaire) and objective (systolic blood pressure reactivity-SBP-R) indices. Results showed that the self-involvement manipulation did not increase self-reported motivation, SBP-R, or task performance. Further correlation analyses focusing on individual differences in motivation did not reveal any association with performance, in either young or older adults. Notably, we found age-related decline in both ER and ToM, despite older adults having higher motivation than young adults. Overall, the present results were not consistent with previous claims that motivation affects older adults' social-cognitive performance, opening the route to potential alternative explanations.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| 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".