Fluency in spontaneous speech predicts individual variance in executive function among seniors
Bibliographic record
Abstract
Abstract Background Several recent studies have used automated linguistic analysis of naturalistic speech in a picture‐description task (e.g. Cookie Theft) combined with machine learning approaches to distinguish the speech of those with dementia from healthy age‐matched controls. Extension of these techniques to predict continuous variables related to cognitive status offers a means to track the severity of dementia over time and evaluate the effectiveness of interventions. Here we examine the relationship of speech variability to executive function, the ability to manage conflicting information in speeded task performance. Higher executive function confers protection against the clinical manifestation of dementia despite underlying neurodegeneration. Method In a new sample of 76 healthy adults aged 65‐75, we measured executive function using an extensive test battery, and elicited spoken picture descriptions in the same individuals. The battery included N‐back, Simon task, verbal fluency, Color Word Interference (CWI or Stroop test), Trails A & B, the elevator subtests of the Test of Everyday Attention, and Logical Memory. Two picture description narratives were recorded and subjected to automated analysis generating over 400 linguistic features, grouped into 8 composite measures. Result Exploratory factor analysis revealed one factor accounting for most variance in the executive function battery. This factor exhibited significant correlations with four of the eight speech composite measures, from strongest to weakest: word finding difficulty, local coherence, lexical richness, and syntactic complexity, reflecting greater fluency in spontaneous descriptive speech. No significant correlations were found with repetitiveness, global coherence, information units, and sentiment. Ongoing analyses include factor characterization of the language measures with a larger normative sample. Conclusion Older individuals with higher executive function, as captured by common laboratory tests, also exhibit more fluent speech as quantified by automated linguistic analysis. Word finding difficulties may reflect overall slowing of cognitive processes and access to long‐term memory. Automated speech analysis may ultimately serve as an inexpensive and repeatable measure to track cognitive status over time in older adults at risk of dementia.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".