Bibliographic record
Abstract
While some research has now started to suggest that there are long-term memory (LTM) deficits in alexithymia, short-term memory (STM) in alexithymia remained largely unexplored. This study investigated whether the STM trace for emotion and neutral words might also be disrupted by alexithymia. Forty-four participants were randomly assigned to Study 1 in which the to-be-memorised six-word lists were composed of words belonging to the same valence (i.e. pure lists condition, Study 1), and 44 other participants were randomly assigned to Study 2 in which six-word lists were composed of embedded neutral and emotional words (i.e. mixed lists condition). All the participants completed the Toronto-Alexithymia Scale (TAS-20) and a current mood states scale (PANAS). Results showed that the main effect of alexithymia was observed in the pure lists condition while no alexithymia groups effect emerged in the mixed lists condition. In the pure lists condition only correlation analyses confirmed that alexithymia significantly and negatively correlated with recall accuracy. The results are discussed with regard to the influence of alexithymia on the proposed role of (1) semantic organisation of LTM on STM performance in the pure lists condition and (2) attentional capture by emotional words in the mixed lists condition.
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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 | 0.000 |
| 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) | 0.004 | 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".