Evoking episodic and semantic details with instructional manipulation during autobiographical recall.
Bibliographic record
Abstract
Older adults tend to describe experiences from their past with fewer episodic details, such as spatiotemporal and contextually specific information, but more nonepisodic details, particularly personal semantic knowledge, than younger adults. While the reduction in episodic details is interpreted in the context of episodic memory decline typical of aging, interpreting the increased production of semantic details is not as straightforward. We modified the widely used Autobiographical Interview (AI) to create a Semantic Autobiographical Interview (SAI) that explicitly targets personal (P-SAI) and general semantic memories (G-SAI) with the aim of better understanding the production of semantic information in aging depending on instructional manipulation. Overall, older adults produced a lower proportion of target details than young adults. There was an intra-individual consistency in the production of target details in the AI and P-SAI, suggesting a trait level in the production of personal target details or consistency in the narrative style and communicative goals adopted across interviews. Older adults consistently produced autobiographical facts and self-knowledge across interviews, suggesting that they are biased toward the production of personal semantic information regardless of instructions. These results cannot be easily accommodated by accounts of aging and memory emphasizing reduced cognitive control or compensation for episodic memory impairment. Nevertheless, future work is needed to fully disentangle between these accounts. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".