Lexical differences between working and resistance sessions in psychoanalysis

Computational psychotherapy research: scaling up the evaluation of patient-provider interactions

In psychotherapy, the patient-provider interaction contains the treatment's active ingredients. However, the technology for analyzing the content of this interaction has not fundamentally changed in decades, limiting both the scale and specificity of psychotherapy research. New methods are required to "scale up" to larger evaluation tasks and "drill down" into the raw linguistic data of patient-therapist interactions. In the current article, we demonstrate the utility of statistical text analysis models called topic models for discovering the underlying linguistic structure in psychotherapy. Topic models identify semantic themes (or topics) in a collection of documents (here, transcripts). We used topic models to summarize and visualize 1,553 psychotherapy and drug therapy (i.e., medication management) transcripts. Results showed that topic models identified clinically relevant content, including affective, relational, and intervention related topics. In addition, topic models learned to identify specific types of therapist statements associated with treatment-related codes (e.g., different treatment approaches, patient-therapist discussions about the therapeutic relationship). Visualizations of semantic similarity across sessions indicate that topic models identify content that discriminates between broad classes of therapy (e.g., cognitive-behavioral therapy vs. psychodynamic therapy). Finally, predictive modeling demonstrated that topic model-derived features can classify therapy type with a high degree of accuracy. Computational psychotherapy research has the potential to scale up the study of psychotherapy to thousands of sessions at a time. We conclude by discussing the implications of computational methods such as topic models for the future of psychotherapy research and practice.

Psychodynamic change in psychotherapy: cycles of patient-therapist linguistic interactions and interventions

Psychodynamic change is understood to occur in part through the unique therapeutic relationship developed between therapist and patient, and the subtle cycles of their conversation from relaxed connection to intense experiencing. The Therapeutic Cycles Model (TCM) (Mergenthaler, 1996 ) and Heidelberg Structural Change Scale (HSCS) (OPD Task Force, 2008 ) were used to investigate therapist-patient dynamic processes across 16 sessions of psychotherapy. The TCM identified interventions of the therapist instigating change in emotion-abstraction patterns. Structural personality change was higher in TCM cycles, and differed according to emotion-abstraction patterns. The interventions of the therapist promoted dynamic structural change in the patient. The findings demonstrate for the first time the interconnection between specific types of therapist and patient dialogue that promote deep changes.

Tumor patients in psychodynamic psychotherapy including daydreaming: can imagery enhance primary process and positive emotions?

This therapy process study investigates the use of guided affective imagery for tumor patients. The therapeutic access to tumor patients is generally described as complex and challenging because of a disturbed emotion regulation and a defensive focus on reality. After autologous blood stem cell transplantation, 29 patients were treated with psychotherapy, including two daydreaming imagery sessions. Three text-analytical measures--Affective Dictionary Ulm, Regressive Imagery Dictionary, and Computerized Referential Activity for verbatim session transcripts--as well as the Quality of Life Questionnaire and the Karnofsky Performance Status were administered. Results show that guided affective imagery was able to enhance the psychotherapeutic process in tumor patients by activating the primary process, decreasing anxiety, and increasing referential activity. The positive emotional shift during imagery was achieved by the patients irrespective of their oncological severity status. Study limitations and future directions for research are discussed.

Toward a quantitative characterization of patient-therapist communication

The efficacy of psychotherapy, in its many and varied forms, is one of the most intensely contested issues in clinical practice. Though many theories have been advanced, quantitative evidence in their defense is limited. This contribution is directed to relatively unambitious objectives. Rather than establish yet another qualitative theory of psychotherapeutic practice, we wish to contribute to the construction of research methodologies that can quantitatively characterize the dynamic patterns of patient-therapist communication. It is hoped that a theoretical understanding of psychotherapy might eventually emerge naturally from a growing body of quantitative data.

Primary process content in paranoid schizophrenic speech

Free speech samples given by paranoid schizophrenics, nonschizophrenic paranoids, and nonpsychotic psychiatric patients were submitted to computerized content analysis. Speech samples were searched for words belonging to the Regressive Imagery Dictionary (Martindale, 1975), which yields a well-validated measure of primary process content. Three word-concreteness dictionaries were also employed. Compared to the other groups, paranoid schizophrenics produced speech higher in primary process content as well as in transitive verb concreteness. Results are consistent with psychoanalytic theory.