Cognitive, emotional and expressive factors determining the quality and variability of mentalization styles
Bibliographic record
Abstract
Introduction In contemporary context the difficulties of making sense of social ambiguity becomes one of the most important appeals for seeking the psychological help. This grounds the importance of studying the mechanism underlying the quality of mentalization and its individual variations. Objectives The objective of the study was to find empirical relations between the quality of mentalization and its cognitive, emotional and expressive mediating factors. Methods (1) The Adult Attachment Interview, scored using Social Cognition and Object Relations-Global rating method for mentalization ability. (2) Group embedded figures test. (3) New Tolerance-Intolerance to ambiguity and (4) Toronto alexithymia scale questionnaires. Twenty participants, aged 18-38, looking for psychological consultation, took part in the study. Results Correlation analysis suggests positive relation between field-independency and tolerance to ambiguity (r = .47; p < .05). The complexity of representations of the mind positively correlates with the understanding of social causality (r = .92; p < .01). The affective quality of relationships’ representations positively correlates with the ability to emotionally invest into relationships (r = .66; p < .01), and with the understanding of social causality (r = .47; p < .05). The ability of emotional investment into relationships also positively correlates with the understanding of social causality (r = .93; p < .01). There is a negative link between the severity of alexithymia and the presence of long-term relationships with a partner (r = -.53; p < .05). Conclusions Mentalization should be understood as a system, with underplaying cognitive, expressive and emotional factors. Disclosure No significant relationships.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 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".