Age differences in cue utilization during prospective and retrospective memory monitoring.
Bibliographic record
Abstract
Memory monitoring is an inferential process that we use to evaluate and make judgments about the contents of our memory. Prior work has shown age-related similarity in prospective monitoring of ongoing memory processes, but age-related deficits when retrospectively monitoring the source of memories. In the current study, we examined how extrinsic and intrinsic cues influence age differences in these 2 forms of memory monitoring. Two experiments were conducted in which young and older adults made prospective judgments of learning (JOLs) to monitor ongoing memory processes as well as retrospective source judgments during retrieval. The emotional valence of words (positive, negative, and neutral) served as an intrinsic cue across experiments. Extrinsic importance cues were manipulated via item-based directed forgetting cues (to-be-remembered versus to-be-forgotten cues) in Experiment 1 and value-based cues (+10 versus -10 cues) in Experiment 2. Results provide novel evidence for age-related similarity in use of extrinsic and intrinsic cues during prospective memory monitoring via JOLs. By contrast, during retrospective source monitoring, older but not young adults showed a bias to attribute positive items to extrinsic cues with higher importance, even when those attributions were inaccurate. These findings suggest that the age-related tendency to favor positive information may lead to systematic errors in retrospective monitoring, which has implications for the credibility of older adults' source judgments when monitoring memory for emotional events. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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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 | 0.007 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".