A discourse-theoretic approach to story recall in aging and mild cognitive impairment
Bibliographic record
Abstract
Recall of story materials is a primary way to assess episodic memory. However, the standard scoring method may not be maximally sensitive to cognitive decline. We developed a set of 24 stories, and younger and older adults heard these stories and recalled them immediately and after a delay (Study 1). Twelve of these stories were then selected, and older adults and people with MCI completed immediate and delayed recall of these stories (Study 2). Responses were classified as veridical, gist, or distorted, and were scored by number of units and number of propositions recalled. Younger adults had higher veridical recall than older adults, and proposition-based scoring revealed higher gist recall in older than younger adults. Gist and distortion recall increased over time in older adults, but decreased in MCI. Using proposition-based scoring and distinguishing between veridical and gist responses may discriminate better between healthy older adults and people with MCI.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".