Natural Speech Analysis Can Reveal Individual Differences in Executive Function Across the Adult Lifespan
Bibliographic record
Abstract
PURPOSE: Automated analysis of naturalistic speech has emerged as an effective tool for detecting cognitive decline in dementia but has seldom been used to examine the ordinary cognitive decline occurring in normal aging. Executive function (EF) declines throughout the adult lifespan but is difficult to track longitudinally due to practice effects, making speech-based assessments particularly attractive. This study examined relationships between EF and speech characteristics. METHOD: We collected two audio picture descriptions from participants in two experiments that also included EF assessments, with 67 healthy older adults (aged 65-75 years) in Study 1 and 174 healthy adults (aged 18-90 years) in Study 2. Language composite scores were computed by aggregating relevant speech features indexing aspects of speech that have been reported to show changes in pathological aging. Principal components reflecting common covariation in speech features were extracted from a large training data set to compute speech domain scores. The relationships between language composites/speech principal components and EF were assessed while controlling for age, gender, and education. RESULTS: In Study 1, older adults' word-finding difficulties, measured as speech disfluencies, showed significant associations with EF. Study 2 confirms that speech disfluencies can explain individual differences in EF not only for adults above the age of 65 years but also across the adult lifespan. Information units and coherence in speech showed weaker associations with EF and Montreal Cognitive Assessment scores that were not significant after correction for multiple comparisons. CONCLUSION: The findings revealed associations between word-finding ability in natural speech and general EF across the adult lifespan, supporting natural speech analysis as a convenient and sensitive assessment of general cognitive ability.
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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 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".