EXPLORING LINGUISTIC PATTERNS ON A STORY RECALL TASK IN PEOPLE WITH MILD COGNITIVE IMPAIRMENT
Bibliographic record
Abstract
Abstract Mild cognitive impairment (MCI) involves declines in language and episodic memory. Episodic memory is often assessed using language tasks. To prevent linguistic factors from confounding recall scores, memory and language should be jointly examined. We explored linguistic patterns on a story recall task among cognitively healthy adults aged 65+ (n=18) and people with amnestic MCI (n=18). Participants completed immediate and delayed (20-30min) recall on a set of novel story recall materials (6 pairs, i.e., 12 stories total). Stories were coded using a propositional coding scheme (where a proposition refers to the smallest unit of meaning), as well as a unit scoring scheme (i.e., individual words). Responses were coded as veridical (word-for-word), gist (general idea), and distortion (error). Linguistic features of the output were coded using the Linguistic Inquiry and Word Count (LIWC) program. Overall, people with MCI produced more verbs, fewer time-related words, and fewer total words than control participants. In the MCI group, delayed unit- and proposition-based veridical and gist recall scores were positively correlated with certainty and causation words, indicating that higher certainty about events and their causal links is associated with better memory. Total words were positively correlated with all immediate and delayed recall scores, indicating that amount of linguistic output is strongly linked to memory in MCI. Time-related words were positively correlated with immediate unit-based veridical recall, suggesting that, in MCI, more words denoting time signal better immediate recall of story details. Examining linguistic features of verbal output in memory tasks could improve detection of 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".