The role of alexithymia in memory and executive functioning across the lifespan
Bibliographic record
Abstract
Alexithymia is a personality trait characterised by difficulties identifying feelings (DIF), describing feelings (DDF), and externally oriented thinking (EOT). Alexithymia has been associated with poorer memory, at least for emotive materials, and recently, with executive and neural dysfunction. Aging is also accompanied by poorer memory and executive functioning (EF), neural dysfunction, and increasing alexithymia. Thus, the hypothesis of a general cognitive impairment in alexithymia, particularly in elders, needs investigation. Three large, independent, cross-sectional experiments ( n = 296, 139 and 121, respectively) investigated memory and EF in healthy adults, ranging from young to old adulthood, with age, sex, and the three Toronto Alexithymia Scale-20 subscales (DIF, DDF, EOT) as predictors in hierarchical regressions. Across studies, alexithymia contributed to poorer memory (via EOT) and EF (via DIF), in younger and older adults. Additionally, these effects occurred in non-emotive contexts with neutral stimuli. Moreover, although memory was worse with greater age and poor EF contributed to poor memory, those who had both high EOT and poor EF had particularly poor memory. Thus, alexithymia (particularly via high DIF or high EOT) is a risk factor for age-related cognitive decline. Further research should clarify the direction and nature of these complex relationships.
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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 | 0.003 |
| 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 | 0.000 | 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".