Alexithymia and representations of others: a study using the Social Cognition and Object Relations Scale (SCORS)
Bibliographic record
Abstract
OBJECTIVES: In this study we investigate the relation between alexithymia, which refers to problems in verbalizing and regulating affects, and representations of others; more specifically we hypothesize that alexithymia will be related to lower scores on clinician-rated scales of the SCORS measuring social cognitions and the affective quality of object relations. METHODS: Correlations between scores of 80 psychiatric inpatients on the self-report Toronto Alexithymia Scale (TAS-20) and the Toronto Structured Interview for Alexithymia (TSIA), and scores on the SCORS (expert ratings based on TAT narratives) were calculated. RESULTS: No correlations between scores on the TAS-20 and the SCORS were observed. We did find significant correlations between the TSIA total score, its subscales Difficulties Identifying Feelings and Difficulties Describing Feelings and the SCORS-scales Complexity of Representations and Social Causality. CONCLUSION: This study confirms early clinical observations of poor representational life in alexithymic patients. However, in contrast with clinical and empirical studies describing blank relationships and a cold interpersonal functioning, no relation was found with affective aspects (affect-tone and emotional investment) of object relations. Therapists should consider these patients’ problems in interpersonal functioning as possibly connected with problems in mentalizing interpersonal relationships, rather than with negative representations of relationships.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".