Classification of general and personal semantic details in the Autobiographical Interview
Bibliographic record
Abstract
The Autobiographical Interview (AI) separates internal (episodic) and external (non-episodic) details from transcribed protocols using an exhaustive and reliable scoring system. While the details comprising the internal composite are centered on elements of episodic memory, external details are more heterogeneous as they are meant to capture a variety of non-episodic utterances: general semantics, different types of personal semantics details, metacognitive statements, repetitions, and details about off topic events. Elevated external details are consistently observed in aging and in neurodegenerative diseases. In the present study, we augmented the AI scoring system to differentiate subtypes of external details to test whether the elevation of these details in aging and frontotemporal lobar degeneration (including mixed frontotemporal/semantic dementia [FTD/SD] and progressive non-fluent aphasia [PNFA]) would be specific to general and personal semantics or would concern all subtypes. Specifically, we separated general semantic details from personal semantic details (including autobiographical facts, self-knowledge, and repeated events). With aging, external detail elevation was observed for general and personal semantic details but not for other types of external details. In frontotemporal lobar degeneration, patients with FTD/SD (but not PNFA) generated an excess of personal semantic details ¬but not general semantic details. The increase in personal but not general semantic details in FTD/SD is consistent with prevalent impairment of general semantic memory in SD, and with the personalization of concepts in this condition. Under standard AI instructions, external details were intended to capture off-topic utterances and were not intended as a direct measure of semantic abilities. Future investigations concerned with semantic processing in aging and in dementia could modify standard instructions of the AI to directly probe semantic content.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".