The temporal unraveling of autobiographical memory narratives in patients with temporal lobe epilepsy or excisions
Bibliographic record
Abstract
Medial temporal lobe epilepsy (TLE), a condition known to affect the integrity and function of medial temporal lobe structures such as the hippocampus, has been shown to disrupt memory for real-life episodes. Here, patients with unilateral TLE, patients who received a unilateral temporal lobe resection to cure TLE, and healthy controls produced free narratives of autobiographical memories (AMs). To assess temporal resolution, narratives were segmented into bits of information, or details, which were classified according to how precisely they could be located within the time course of the AM. Categories included details corresponding to the entire AM, to parts or subevents within the AM, and to actions taking place within seconds to minutes. The number of details per category was tallied and compared between patients and controls. Temporal order was assessed by determining the correct (internally consistent) chronological order of the sequence of events within the narrative. Results indicate that while patients' memory for the parts or subevents of personal episodes was intact, as was their temporal order, their memory for the minute-by-minute unraveling of the episode was impaired. We believe this loss of temporally specific details may contribute to the reduced vividness of AM recollection in TLE patients. Our findings provide further evidence that patients with hippocampal damage retrieve skeletal AMs for which the gist of the memory is maintained, but the specific details are lost.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".