Evoking Episodic and Semantic Details with Instructional Manipulation: the Semantic Autobiographical Interview
Bibliographic record
Abstract
Most measures of naturalistic human memory instruct participants to recall personally-experienced episodes in narrative format. These narratives contain non-episodic details, such as general knowledge of the world, or personal knowledge about one’s life circumstances that are elevated with aging. As this non-episodic content is incidental to the instructions, it is difficult to interpret. We modified the widely used Autobiographical Interview (AI) to create a Semantic Autobiographical Interview (SAI) that explicitly targets personal semantic (P-SAI) and general semantic memories (G-SAI). We tested the SAI in young and older adults, alongside with the original AI. Older adults produced a higher proportion of off-task utterances (i.e., details not probed by instructions) across all sections of the interview. Specifically, older adults produced more autobiographical facts in the AI, more episodic and general semantic details in the P-SAI, and more self-knowledge in the G-SAI than did young adults. However, older adults also consistently produced more probed autobiographical facts than did young adults on the P-SAI. These findings suggest that the increased production of semantic details in ageing reflects a bias towards age differences in autobiographical recall that goes beyond episodic remembering, as reflected by an age-associated abundance of semantic details across sections of the interview, findings that are not accommodated by accounts of aging and memory emphasizing reduced cognitive control or compensation for episodic memory impairment.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".