The influence of alexithymia on memory for emotional faces and realistic social interactions
Bibliographic record
Abstract
High levels of alexithymia are associated with impaired memory for emotional, but not neutral words. Two experimental studies were conducted to determine if a similar memory deficit would be observed for non-verbal socially-relevant stimuli. Thirty-nine female undergraduates (study 1) viewed a series of photographs of faces with different expressions (neutral, angry, happy or sad) and 38 female students (study 2) viewed film-clips of realistic social interactions, which were either neutral in tone or featured anger, happiness or sadness. Participants were asked to identify the emotion portrayed and were subsequently given a recognition memory test for these stimuli. They also completed the Toronto Alexithymia Scale (TAS-20) and the Hospital Anxiety and Depression scale (HADS). Memory for angry faces was negatively related to alexithymia (‘difficulty describing feelings’ (DDF) subscale of the TAS-20. Similarly, memory for realistic social interactions featuring anger, happiness and sadness was negatively related to alexithymia (‘difficulty identifying feelings’ (DIF) and DDF of the TAS-20). These memory deficits were evident in the conscious recollection of the stimuli and were independent of the effects of mood. Our findings are largely consistent with studies using verbal material and confirm that alexithymia is related to deficits in the conscious recollection of emotional material.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".