Mentalization and autobiographical memory as clinical components of the self and identity
Bibliographic record
Abstract
Psychoanalysts have suggested that a mentalization process is required for the transformation of drives and affects into mental representations of self and others. Meanwhile, neuroscientists suggest that episodic memory, through autonoetic consciousness, is crucially involved in the elaboration of the Self and subjective experience. This research empirically investigates the relationship between the quality of mentalization and the efficacy of autobiographical recall, and considers childhood trauma and psychopathology as possible related factors. Thirty participants presenting with either high or low mentalization profiles according to the Mental States Rating Systems (MSRS) were submitted to the TEMPau, a semi-structured interview designed to assess the quality of autobiographical recall from several criteria (episodicity, self-perspective and consciousness). Childhood trauma was assessed using the Childhood Trauma Questionnaire (CTQ). The Symptom Checklist (SCL- 90-R) and the Structured Clinical Interview for DSM-IV Personality Disorders (SCID-II) were used to assess global symptomatology and Axis-II disorders respectively. As expected, levels of mentalization are related to higher levels of childhood abuse (emotional, physical and sexual) and symptomatology (SCL-90-R Global Severity Index). However, contrary to our expectation, better autobiographical memory is associated with poor mentalization profiles, although spontaneity of recall is less efficient in those subjects. No significant relation was found between Axis-II disorders and traits, and other clinical variables.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".