Effect of situation similarity on younger and older adults’ episodic simulation of helping behaviours
Bibliographic record
Abstract
Similar cognitive processes enable us to remember the past (i.e., episodic memory) and simulate future events (i.e., episodic simulation). In the current study, we demonstrate an important role for previous experience when younger and older adults simulate future behaviours. Participants read short descriptions of a person in need of help in scenarios that were more familiar to either younger or older adults (e.g., dealing with dating apps vs writing a cheque). Participants either imagined helping the person or thought about the style of the story (control task), and then rated their willingness to help, scene vividness, emotional concern, and subjective use of theory of mind. Hierarchical mixed effect modelling revealed that both episodic simulation and one's previous experience increased willingness to help, in that participants were more willing to help if they imagined helping and the situation was more familiar to them. Further, in simulated scenarios the relationship between previous experience and willingness to help was mediated by scene vividness and perspective-taking in younger adults, but only by perspective-taking in older adults. Taken together, these findings suggest that situation similarity and episodic simulation increase willingness to help, possibly via different mechanisms in younger and older adults.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".