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.
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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".