Interference Resolution in Emotional Working Memory as a Function of Alexithymia
Bibliographic record
Abstract
Although alexithymia is recognized as a set of traitlike deficits in emotion processing, research suggests there are concomitant cognitive issues as well, including what appears to be an unusual pattern of enhanced working memory (WM) despite broader executive dysfunction. It is unknown whether this enhancement includes the executive elements of WM and whether executive control of WM in alexithymia differs for emotional and neutral stimuli. This study examined how alexithymia moderates patterns of interference resolution in WM with valenced and nonvalenced stimuli. Participants (N = 93) completed the Toronto Alexithymia Scale and a recency probes WM task containing positive, negative, and neutral stimuli, with some trials containing proactive interference from previous trials. The reaction time difference between interference and noninterference trials indexed degree of interference resolution. Toronto Alexithymia Scale score moderated a within-subject effect such that, when valenced probes were used, there was less proactive interference in the positive relative to negative valence condition; this valence-based interference discrepancy was significant for a subset of highly alexithymic participants. Alexithymia did not moderate proactive interference to negative or neutral stimuli or accuracy of responses. These results suggest that, although alexithymia does not influence executive control in WM for nonemotional items, alexithymic people demonstrate an idiosyncratic response to positive stimuli that might indicate blunted reactivity.
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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) | 0.001 | 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".