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

Tasca, Anthony N; Carlucci, Samantha; Wiley, James Christopher; Holden, Matthew S.; El-Roby, Ahmed; Tasca, Giorgio A.

doi:10.1080/10503307.2022.2156306

Cited by 6 publications

(6 citation statements)

References 39 publications

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“…Mutual affect analysis. Five interaction studies examined provider-therapist emotional convergence [ 131 , 132 ] during the intervention, including defense mechanisms [ 133 ] and humor [ 45 ]. Tanana and colleagues examined a large corpus of therapy transcripts [ 102 ], and showed that an attention-based architecture captured therapists’ and patients’ valence interactions and their context more accurately ( K = 0.48) than previous lexicon methods ( K = 0.25 and K = 0.31) and human raters ( K = 0.42).…”

Section: Resultsmentioning

confidence: 99%

Natural language processing for mental health interventions: a systematic review and research framework

Malgaroli,

Hull,

Zech

et al. 2023

Transl Psychiatry

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Neuropsychiatric disorders pose a high societal cost, but their treatment is hindered by lack of objective outcomes and fidelity metrics. AI technologies and specifically Natural Language Processing (NLP) have emerged as tools to study mental health interventions (MHI) at the level of their constituent conversations. However, NLP’s potential to address clinical and research challenges remains unclear. We therefore conducted a pre-registered systematic review of NLP-MHI studies using PRISMA guidelines (osf.io/s52jh) to evaluate their models, clinical applications, and to identify biases and gaps. Candidate studies (n = 19,756), including peer-reviewed AI conference manuscripts, were collected up to January 2023 through PubMed, PsycINFO, Scopus, Google Scholar, and ArXiv. A total of 102 articles were included to investigate their computational characteristics (NLP algorithms, audio features, machine learning pipelines, outcome metrics), clinical characteristics (clinical ground truths, study samples, clinical focus), and limitations. Results indicate a rapid growth of NLP MHI studies since 2019, characterized by increased sample sizes and use of large language models. Digital health platforms were the largest providers of MHI data. Ground truth for supervised learning models was based on clinician ratings (n = 31), patient self-report (n = 29) and annotations by raters (n = 26). Text-based features contributed more to model accuracy than audio markers. Patients’ clinical presentation (n = 34), response to intervention (n = 11), intervention monitoring (n = 20), providers’ characteristics (n = 12), relational dynamics (n = 14), and data preparation (n = 4) were commonly investigated clinical categories. Limitations of reviewed studies included lack of linguistic diversity, limited reproducibility, and population bias. A research framework is developed and validated (NLPxMHI) to assist computational and clinical researchers in addressing the remaining gaps in applying NLP to MHI, with the goal of improving clinical utility, data access, and fairness.

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Section: Resultsmentioning

confidence: 99%

Natural language processing for mental health interventions: a systematic review and research framework

Malgaroli,

Hull,

Zech

et al. 2023

Transl Psychiatry

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“…Another study examined the quality of romantic relationships during COVID-19 based on experiences reported in the ECR-R self-report questionnaire, along with other demographic factors, analyzed together using cross-validated machine learning models: Least Absolute Shrinkage and Selection Operator (LASSO) and Extremely Randomized Trees (Ex-traTrees) [39]. Tasca et al also conducted a study where they developed automated coding of defense mechanisms from AAI transcripts using Robustly Optimized Bidirectional Encoder Representations from Transformers Pre-Training Approach (RoBERTa) [7].…”

Section: Related Workmentioning

confidence: 99%

“…In the second approach, research generally focuses on identifying non-verbal aspects, often not analyzed in psychometric observations. Several studies have explored nonverbal behaviors in the AAI procedure, which initially in their scoring system only analyze verbal and discourse coherence [30,31,7,32]. Zeng et al observed multimodal behavior traits (facial expression & speech prosody) [30].…”

Section: Related Workmentioning

confidence: 99%

“…Previous studies have shown that attachment insecurity commonly emerges as one of the risk factors of psychopathology, ranging from mild distress to severe mental illness. For instance, attachment insecurities (both anxious and avoidant) have been linked to depression, generalized anxiety, social anxiety, academic distress, substance use [3,4], obsessivecompulsive disorder [5], suicidal tendencies [6], eating disorders [7], borderline personality disorder [8], bipolar disorder [9], schizophrenia [10], and post-traumatic stress disorder (PTSD) [11,12]. In light of the wide variety of psychopathology influenced by insecurity, prevention and treatment efforts have become a common focus for researchers.…”

mentioning

confidence: 99%

“…Previous studies have generally focused on developing models to observe adult attachment in AAI procedures. These studies have different research aims, such as emotion recognition during AAI based on audio-visual data [30], facial expression and movement observation related to attachment and mental disorders [31], automated coding of AAI transcripts for defense mechanism detection [7], and exploration of the association between verbal content and prosodic features during AAI [32].…”

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confidence: 99%

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Attach-SwiNet: Multimodal Attachment Style Classification Model Based on Non-Verbal Signals

Maghfira,

Basaruddin,

Alfa Krisnadhi

et al. 2024

IEEE Access

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Attachment systems, representing emotional bonds with significant others, have emerged as an important aspect of psychology that influences self-development and social interactions. Adult attachment studies in psychology have mostly relied on questionnaires and interviews, primarily centered on romantic relationships and parent-child dynamics during childhood. In machine learning-based adult attachment studies, the assessment of objective aspects of non-verbal behaviors is also investigated, but how these behavioral characteristics are associated with attachment remains unexplored. This paper introduces a new multimodal model for attachment style classification, focusing on close relationships established within young adults. Our proposed model integrates representations of emotions derived from non-verbal behaviors alongside subjective responses to classify attachment styles. Leveraging pre-trained Swin Transformers for capturing emotion associations in facial expression videos and pre-trained ResNet50 for analyzing speech responses, we fuse the best emotion representations from both datasets with rating data from the Experiences in Close Relationships -Relationship Structures (ECR-RS) for attachment style classification. The experimental results demonstrate that the proposed model enhances the performance of unimodal behavioral data and subjective questionnaire responses by 1.13%.

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Art‐Based, Continuous Measurement for the Attachment Paradigm: Development and Implementation of a Digital Application

Unger,

Ostermann

2024

Integrated Science

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

Cited by 6 publications

References 39 publications

Natural language processing for mental health interventions: a systematic review and research framework

Natural language processing for mental health interventions: a systematic review and research framework

Attach-SwiNet: Multimodal Attachment Style Classification Model Based on Non-Verbal Signals

Art‐Based, Continuous Measurement for the Attachment Paradigm: Development and Implementation of a Digital Application

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