Alexithymia and facial expression recognition: A systematic review and meta-analysis
Bibliographic record
Abstract
The primary aim of this systematic review and meta-analysis was to estimate the strength of the relationship between alexithymia and facial expression recognition. Secondary aims were to determine if the strength of the relationship was moderated the type of stimuli (e.g., dynamic, static) used in the study, and whether studies excluded participants with mental health disorders. Web of Science, PsycINFO, MEDLINE, and Scopus database searches were conducted on 21 st June 2024. Studies were included if they comprised participants aged between 16 and 65 years, included a validated measure of alexithymia, a forced choice facial expression recognition labelling task, and reported the relationship between these variables. Risk of bias was assessed using the AXIS tool. Twenty-four studies were included in a random effects meta-analysis revealing a significant, medium, negative relationship between alexithymia and overall facial expression recognition ability, r = −0.24, CI [−0.29, −0.18]. Small, negative relationships were observed for recognition of anger, disgust, fear, happiness, and sadness. The type of stimuli moderated the strength of the relationship, with significant negative relationships between alexithymia and recognition of static facial expressions observed, but no significant relationship observed for dynamic stimuli. Exclusion of participants with mental health disorders did not moderate the strength of the relationship. Results indicate alexithymia is associated with a global deficit labelling static facial expressions, that does not appear to be attributable to comorbid mental health disorders. Further research is needed to clarify the relationship between alexithymia and recognition of dynamic facial expressions. • There is a significant, medium relationship between alexithymia and facial expression recognition • The relationship between alexithymia and facial expression recognition was moderated by stimuli type • There was a significant medium relationship for static facial expressions, but no relationship observed for dynamic facial expressions. • The relationship between alexithymia and facial expression recognition remained when participants with mental health disorders were excluded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".