Applying the Relevance Realization Theory of Cognitive Function to Moral Decision-Making Across Adulthood
Bibliographic record
Abstract
One of the markers of human cognitive agency is the capacity for moral cognition. Across a range of societies, older adults are perceived as more moral than younger adults. Interestingly, research shows that compared to younger adults, older adults are more likely to endorse deontological (as opposed to utilitarian) ethics. This age difference has been explained in terms of cognitive decline and socioemotional shifts. In this paper, I consider the Exploration-Exploitation (EE) model of aging, as well as the Relevance Realization (RR) framework, in explaining moral decision-making phenomena across younger and older adults. My analysis reveals that in older age, the mechanisms underlying moral decision-making gravitate toward efficiency-based processing more generally, as opposed to exploitation-based processing more specifically, thereby lending support in favour of the RR framework over the EE model of aging. I conclude that in light of the explanatory breadth offered by RR, the opponency of exploration-exploitation alone, a trade-off relationship subsumed by RR, is at times necessary but insufficient in explaining age differences in response to sacrificial moral dilemmas. Accordingly, I suggest that augmenting EE with RR could expand both its theoretical precision and explanatory power, while ameliorating its possible theoretical blind spots. I conclude by discussing the empirical limitations of the RR framework and proposing potential empirical avenues for future research on RR in the context of moral decision-making.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".