Linguistic markers of story recall can help differentiate mild cognitive impairment from normal aging
Bibliographic record
Abstract
Mild cognitive impairment (MCI) involves a decline in episodic memory and, in many cases, language. Taler et al. (2021) developed a set of story recall materials that we expected to be sensitive to changes in language in normal aging and MCI. Here, we examined the lexical (word-level) contents of participants’ story recall responses from Taler et al. (2021). First, we compared the lexical features of story recall responses between young adults (YA; n = 22), healthy older adults (OA; n = 38), and people with MCI ( n = 17) using the Linguistic Inquiry and Word Count (LIWC) program. Second, we explored the associations between these linguistic variables and story recall in each group. People with MCI produced fewer words overall, as well as higher proportions of verbs and pronouns on immediate recall compared to both YAs and OAs. OAs also produced higher proportions of auxiliary verbs than YAs. Story recall scores were positively correlated with total word count in YA and MCI groups. In YAs only, adjectives were positively correlated with recall. In OAs, recall scores were negatively correlated with proportion of verbs. Our results suggest that the LIWC program paired with our novel story recall task may help identify linguistic markers of normal aging and MCI. Some aspects of language use during story recall may also be related to episodic memory in cognitively healthy individuals and people with MCI. Our findings may have implications for the optimization of MCI screening tools to detect changes in language.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".