Alexithymia and Executive Function in Younger and Older Adults
Bibliographic record
Abstract
OBJECTIVE: Alexithymia is associated with adverse mental and physical health outcomes. Determining neuropsychological factors associated with alexithymia may aid in elucidating its underlying mechanisms and identifying treatment targets. Accumulating evidence indicates that executive dysfunction may co-occur with alexithymia in younger adults (YA). However, research on this link in older adults (OA), who may be at greater risk for alexithymia, is scant. This study determined associations between alexithymia and executive function (EF) in healthy younger and OA. Alexithymia was predicted to be associated with poorer EF in both age-groups. METHOD: Younger (n = 65, aged 18-30; 46% female) and OA (n = 44, aged 61-92; 73% female) completed the 20-item Toronto Alexithymia Scale, three EF tasks (Verbal Fluency, Design Fluency, and Trail Making), measures of memory and verbal IQ, and a self-report measure of depressive symptoms. Three EF composites were created to assess verbal EF, visuospatial EF, and global EF. RESULTS: Greater alexithymia and difficulty describing feelings were associated with poorer verbal EF in OA (p = .02 and p = .005, respectively) but not in YA (ps > .05). The other neuropsychological measures were not significantly associated with alexithymia in regression analyses. CONCLUSIONS: Findings are consistent with previous research identifying links between EF and alexithymia. The association between alexithymia and verbal EF may be due to shared prefrontal circuitry involved in emotion regulation. Results provide insight into possible origins of emotion self-awareness deficits in OA.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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".