Remembering without a past: Individuals with anterograde memory impairment talk about their lives
Bibliographic record
Abstract
This paper describes the linguistic resources people with anterograde amnesia draw on in conversational narratives. Because of their problems in recollecting post-morbid memories, it is particularly challenging for such individuals to refer to personal experiences. Seven patients with anterograde memory impairments due to neurotrauma were interviewed one year post-event. Among other topics, they were asked to talk about their new lives and selves, which was expected to be a precarious affair given that they did not have many or any autobiographical memories. Microanalyses of their narratives identified three readily available linguistic resources that participants used to facilitate their storytelling. These were categorized as "memory importation" (transplanting a past memory into the present), "memory appropriation" (taking another's memory as one's own), and "memory compensation" (searching for memories). It is argued that although these resources were not always efficiently used by participants and their use often violated conversational expectations, these linguistic techniques provided a helpful means to sustain the production of personal narratives, even in the absence of autobiographical memory.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".