Is Splitting Related to Resistance to Proactive Interference? A Process-Oriented Study of Kernberg’s Conceptualization of Splitting
Bibliographic record
Abstract
INTRODUCTION: Splitting, as a defense mechanism in Kernberg's theory, plays a significant role in the development and maintenance of polarized and oscillating representations of self/other characteristics of borderline personality disorder (BPD). Although the notion of splitting can be considered from a structural and a functional point of view, almost all empirical studies to date have focused on the former elements to the detriment of related cognitive processes. METHODS: To further investigate the cognitive processes related to splitting, 60 participants were administered the Splitting Index and indexes of resistance to proactive interference (PI) using the interpersonal recent negative task with words that reflect negative or positive interactions compared to neutral words. RESULTS: The use of splitting was uniquely and significantly predicted by a higher capacity to resist PI and a lower capacity to consistently maintain this resistance when presented with negative words, above and beyond BPD traits, primitive defenses, and the presentation of neutral words. Results showed no evidence of a relationship between splitting and resistance to PI with positive words. CONCLUSION: Results appear compatible with Kernberg's conceptualization of splitting as an active defense process that relates to an unstable capacity to inhibit negative representations of the object from entering working memory.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".