Language and memory: an investigation of the relationship between autobiographical memory recall and narrative production of semantic and episodic information
Bibliographic record
Abstract
Background: The production of autobiographical narratives requires linguistic structures and the ability to access and generate both semantic information and episodic details of personal events.Aims: This study investigated autobiographical narratives produced by individuals with established semantic memory impairments (semantic variant primary progressive aphasia; svPPA) or episodic memory impairments (amnestic mild cognitive impairment; aMCI) in order to investigate whether different categories of memory impairment would manifest different linguistic deficits.Methods & Procedures: We used the Autobiographical Interview and Quantitative Production Analysis methods to investigate linguistic production during autobiographical recall. Additional investigations compared the production of present and past tense inflections in order to look for morpho-syntactic differences in the sets of episodic and semantic information.Outcomes and Results: The results showed that individuals with svPPA produced fewer well-formed sentences when producing episodic details and produced fewer past tense inflections when producing semantic details in comparison to healthy controls. The aMCI group produced fewer episodic utterances but produced a larger number of words in the set of semantic details, in comparison to healthy controls.Conclusions: It is possible that specific demands related to the type of message being conveyed, or high cognitive load during retrieval of episodic information may affect the narration process. Difficulty in the retrieval of episodic information is likely related to reduced production of episodic utterances in individuals with aMCI and may be related to deficits in linguistic production in svPPA. We propose that the results in the set of semantic details are connected to previous findings relating to semantic memory and deficits in discourse coherence in both groups.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".