Bibliographic record
Abstract
The focus of this dissertation is on future prospection and goals in healthy aging. More broadly, this dissertation is contextualized within a particular theoretical framework that proposes that some of the cognitive decline associated with aging reflects adaptive changes in motivation and resource allocation (Hess, 2014). We used prospection as the cognitive domain within which to explore these motivational changes. In particular, we looked at a subtype of prospection known as episodic future thinking, which is concerned with how individuals imagine future scenarios, and what effects this might have on behaviour. Whereas the first experiment took up questions about motivational influences on the nature and bounds of future thinking deficits in older adults, the second experiment tested hypotheses about the adaptive function of future thinking. Specifically, episodic future thinking is often presumed to be adaptive for planning and successful action execution. Using prospective memory as a platform, we tested the idea that episodic future thinking can facilitate goal achievement, specifically in older adults. In the first experiment, a cue-word paradigm, with words chosen to reflect age-relevant goal domains, was used to prompt the imagination of future scenarios. The results from the first experiment suggest that contrary to predictions, goal activation does not affect level of episodic detail in future thinking. However, phenomenological characteristics were modulated, with younger adults showing more sensitivity than older adults. In the second experiment, participants employed different cognitive strategies – one of which included future thinking – in a gold standard test of prospective memory. The results from the second experiment failed to find episodic future thinking a helpful strategy and did not replicate previous work demonstrating a beneficial effect of another commonly used strategy, implementation intentions, which was used for comparison. Nonetheless, secondary analyses suggest that individual differences in strategy preference may be critical to consider before ruling out the utility of episodic future thinking. The results from these experiments contribute to a growing literature on motivation-cognition interactions across the lifespan, and suggest promising future directions regarding research into motivation, prospection, and healthy aging.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".