Age Differences in Hindsight Bias: The Role of Episodic Memory and Inhibition
Bibliographic record
Abstract
UNLABELLED: BACKGROUND/STUDY CONTEXT: After learning an event's outcome, people's recollection of their former prediction of that event shifts towards the actual outcome. This hindsight bias (HB) phenomenon tends to be stronger in older compared with younger adults; however, it is unclear whether age-related changes in other cognitive abilities mediate this relationship. METHODS: Sixty-four younger adults (Mage = 20.1; range = 18-25) and 60 community-dwelling older adults (Mage = 72.5; range = 65-87) completed a memory design HB task. Two aspects of HB, its occurrence and magnitude, were examined. Multiple regression and mediation analyses were conducted to determine whether episodic memory and inhibition mediate age differences in the occurrence and magnitude of HB. RESULTS: Older adults exhibited a greater occurrence and magnitude of HB as compared with younger adults. The present findings revealed that episodic memory and inhibition mediated age-related increases in HB occurrence. Conversely, neither cognitive ability mediated age-related increases in HB magnitude. CONCLUSION: Older adults' susceptibility to the occurrence of HB is partly due to age-related declines in episodic memory and inhibition. Conversely, age differences in the magnitude of HB appear to be independent of episodic memory and inhibition. These findings have important implications for understanding the mechanisms by which susceptibility to HB changes across the adult life span.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".