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Detecting defense mechanisms from Adult Attachment Interview (AAI) transcripts using machine learning.

Anthony N Tasca1, Samantha Carlucci2,3, James C Wiley4

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Summary

Machine learning models can assist in identifying defense mechanisms from interviews, but are not yet proficient enough to replace human coders. Further research is needed to explore their practical applications in clinical psychology.

Keywords:
Defense Mechanism Rating ScaleRoBERTaconversation analysisdefensive functioningmachine learning

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Area of Science:

  • Psychology
  • Computer Science
  • Computational Linguistics

Background:

  • Defensive functioning, an unconscious process for managing threats, is linked to psychopathology.
  • Current assessment relies on resource-intensive human coding of defense mechanisms from interviews.

Purpose of the Study:

  • To develop and evaluate machine learning models for automating the coding of defense mechanisms in interview transcripts.
  • To assess the performance of RoBERTa-based models in detecting specific defenses.

Main Methods:

  • Trained and evaluated five RoBERTa-based machine learning models on 16,785 interviewer-participant talk-turn pairs from 192 interviews.
  • Models were designed to detect any defense and four specific defenses: repression, intellectualization, reaction formation, and undoing.

Main Results:

  • Models demonstrated capability in distinguishing defenses (ROC-AUC .82-.90).
  • Performance was insufficient to replace human coders (PR-AUC .28-.60).
  • Follow-up analysis explored other practical uses for the models.

Conclusions:

  • Machine learning models show potential for assisting human coders in defense mechanism identification.
  • Future research should focus on deployment studies to confirm efficiency gains in human coding.