The perceptual richness of complex memory episodes is compromised by medial temporal lobe damage
Bibliographic record
Abstract
Perceptual richness, a defining feature of episodic memory, emerges from the reliving of multimodal sensory experiences. Although the importance of the medial temporal lobe (MTL) to episodic memory retrieval is well documented, the features that determine its engagement are not well characterized. The current study assessed the relationship between MTL function and episodic memory's perceptual richness. We designed a laboratory memory task meant to capture the complexity of memory for life episodes, while manipulating memory's perceptual content. Participants encoded laboratory episodes with rich (film clips) and impoverished (written narratives) perceptual content that were matched for other characteristics such as personal significance, emotionality and story content. At retrieval, participants were probed to describe the stories' perceptual features and storyline. Participants also recalled autobiographical memories (AMs) in a comparison condition. We compared the performance of patients with unilateral medial temporal lobe epilepsy (mTLE) and healthy controls to assess how damage to the MTL affects retrieval in these conditions. We observed an overall decrease in detail count in the mTLE group, along with a disproportionate deficit in perceptual details that was most acute in the AM and the perceptually enriched film clip conditions. Our results suggest that the impaired sense of reliving the past that accompanies MTL insult is mediated by a paucity of perceptual episodic memory details. We also introduce a new protocol that successfully mimics naturalistic memories while benefiting from the experimental control provided by using laboratory stimuli.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".