Maternal attachment and mind-mindedness: the role of emotional specificity
Bibliographic record
Abstract
We explored the relation between maternal mind-mindedness (i.e., a mother's tendency to verbally refer to her infant's mental world through use of infant-directed mental state terms) and maternal attachment. Mothers (N = 76), classified prenatally as Autonomous, Dismissing, Preoccupied, and Unresolved using the Adult Attachment Interview (AAI), simulated speaking to their 6-month-old infants in positive and negative emotion contexts. Mothers' utterances were coded for frequency of use of emotion and cognition-related mind-minded terms. Results indicated a significant negative relation between coherence of mind scores on the AAI and emotion mind-mindedness in the positive emotion context. When differences between insecure attachment categories and mind-mindedness were explored, results indicated that mothers with Preoccupied attachments were significantly more likely to use emotion-related terms than mothers with Dismissing attachments and that these differences were most pronounced in the negative emotion context. A similar pattern was found for mothers with Unresolved attachments compared to those with organized (Autonomous, Dismissing, Preoccupied) attachment classifications, however use of emotion mind-minded terms did not differ by emotional context. Future research directions highlighting the importance of exploring the unique contribution of Preoccupied, Dismissing and Unresolved attachment and emotional context in the exploration of mind-mindedness are discussed.
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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 | 0.009 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".