Therapeutic Chaos

Frontiers in psychology, 2017

Objective: The aim of this article is to outline the role of chaotic dynamics in psychotherapy. Besides some empirical findings of chaos at different time scales, the focus is on theoretical modeling of change processes explaining and simulating chaotic dynamics. It will be illustrated how some common factors of psychotherapeutic change and psychological hypotheses on motivation, emotion regulation, and information processing of the client's functioning can be integrated into a comprehensive nonlinear model of human change processes. Methods: The model combines 5 variables (intensity of emotions, problem intensity, motivation to change, insight and new perspectives, therapeutic success) and 4 parameters into a set of 5 coupled nonlinear difference equations. The results of these simulations are presented as time series, as phase space embedding of these time series (i.e., attractors), and as bifurcation diagrams. Results: The model creates chaotic dynamics, phase transition-like...

Clinical Psychology Review, 2007

The study of discontinuities and nonlinear change has been a fruitful endeavor across the sciences, as these shifts can provide a window into the organization of complex systems and the processes that are associated with transition. A common assumption in psychotherapy research has been that change is gradual and linear. The research designs and statistics used to study change often reflect this assumption, but some recent research reveals other patterns of change. We briefly review relevant literature on dynamical systems theory and on life transition and post-traumatic growth to highlight the significance of nonlinear and discontinuous change across areas of psychology. We describe recent applications of these ideas and methods to the study of change in psychotherapy and encourage their use to complement more traditional clinical trial designs. Some change can be gradual and incremental, but many systems in nature show periods of turbulence and instability, with dramatic changes or growth spurts. Ilya Prigogine, a Nobel laureate known for his theory of dissipative structures in chemistry, argues that instabilities play an important role in transformation and that "most of reality, instead of being orderly, stable, and equilibrial, is seething and bubbling with change, disorder, and process" (Prigogine & Stengers, 1984, p. xv). The study of discontinuities has been a fruitful endeavor across the sciences, as these shifts can provide a window into the organization of a system and the processes that are associated with transition.

Mathematical modeling and computer simulations are important means to understand the mechanisms of psychotherapy. The challenge is to design models which not only predict outcome, but simulate the nonlinear trajectories of change. Another challenge is to validate them with empirical data. We proposed a model on change dynamics which integrates five variables (order parameters) (therapeutic progress or success, motivation for change, problem severity, emotions , and insight) and four control parameters (capacity to enter a trustful cooperation and working alliance, cognitive competencies and mindfulness, hopeful-ness, behavioral resources). The control parameters modulate the nonlinear functions interrelating the variables. The evolution dynamics of the system is determined by a set of nine nonlinear difference equations, one for each variable and parameter. Here we outline how the model can be tested and validated by empirical time series data of the variables, by time series of the therapeutic alliance, and by assessing the input onto the system as it is perceived by the client. The parameters are measured by questionnaires at the beginning and at the end of the treatment. A key element of the validation algorithm is the adjustment of the parameter values as assessed by the questionnaires to model-specific parameter values by which the dynamics can be reproduced (calibration). The validation steps are illustrated by the data of a client who used an internet-based tool for high-frequency therapy monitoring (daily self-ratings). Especially after applying the input vector (interventions as experienced by the client) the similarity between the empirical and the model dynamics becomes evident. The averaged correlation between the empirical and the simulated dynamics across all variables is .41, after applying a short averaging mean window and eliminating an initial transient period, it is .62, varying between .47 and .81, depending on the variable. The discussion opens perspectives on the combination of mathematical modeling with real-time monitoring in order to realize data-driven simulations for short-term predictions and to estimate the effects of interventions before real interventions are applied.

This paper provides a general framework for the use of Theory of Dynamic Systems (TDS) in the field of psychotherapy research. Psychotherapy is inherently dynamic, namely a function of time. Consequently, the improvement of construct validity and clinical relevance of psychotherapy process research require the development of models of investigation allowing dynamic mappings of clinical exchange. Thus, TDS becomes a significant theoretical and methodological reference. The paper focuses two topics. First, the main concepts of TDS are briefly introduced together with a basic typology of approaches developed within this domain. Second, we propose a repertoire of investigation strategies that can be used to capture the dynamic nature of clinical exchange. In this way we intend to highlight the feasibility and utility of strategies of analysis informed by TDS.

Clinical Psychological Science, 2019

Whereas sudden gains and losses (large shifts in symptom severity) in patients receiving psychotherapy appear abrupt and hence may seem unexpected, hypotheses from complex-systems theory suggest that sudden gains and losses are actually preceded by certain early-warning signals (EWSs). We tested whether EWSs in patients’ daily self-ratings of the psychotherapeutic process predicted future sudden gains and losses. Data were collected from 328 patients receiving psychotherapy for mood disorders who completed daily self-ratings about their therapeutic process using the Therapy Process Questionnaire (TPQ). Sudden gains and losses were classified from the Problem Intensity scale of the TPQ. The other items of the TPQ were used to compute the EWSs. EWSs predicted an increased probability for sudden gains and losses in a 4-day predictive window. These results show that EWSs can be used for real-time prediction of sudden gains and losses in clinical practice.

BMC Medicine

Background A growing body of research highlights the limitations of traditional methods for studying the process of change in psychotherapy. The science of complex systems offers a useful paradigm for studying patterns of psychopathology and the development of more functional patterns in psychotherapy. Some basic principles of change are presented from subdisciplines of complexity science that are particularly relevant to psychotherapy: dynamical systems theory, synergetics, and network theory. Two early warning signs of system transition that have been identified across sciences (critical fluctuations and critical slowing) are also described. The network destabilization and transition (NDT) model of therapeutic change is presented as a conceptual framework to import these principles to psychotherapy research and to suggest future research directions. Discussion A complex systems approach has a number of implications for psychotherapy research. We describe important design considera...

Psychotherapy Research, 2019

A novel methodology for the empirical analysis of processes in psychotherapy was developed and tested. This method is based on the Fokker-Planck equation (FPE), a probabilistic model that detects the deterministic and stochastic components of a process. The deterministic component is given by the potential function underlying the process. The FPE application can be used to visualize the attractor (or in the case of multistability, attractors) of the dynamics, and the sources of stochasticity. The FPE app can also be employed in two-dimensional systems, for example, client's and therapist's coupled processes; then the method is run on the cross-correlations of the time series. Signatures were defined that merge the functions retrieved from the methodology into numerical values, and may serve to detect associations with conventional self-report measures of psychotherapy. The method was tested in a case series where client's and therapist's heart rate, heart rate variability and respiration were monitored in 20 psychotherapy sessions. The FPE app works well with time series of high resolution and adequate observation numbers, which renders it applicable to nonverbal and physiological time series.

Counselling and Psychotherapy Research

Objective: Current approaches of routine outcome monitoring (session-by-session measures) expect that trajectories of change should move on a standard track. Patients moving out of standard tracks are assumed to be at risk of deterioration. From a nonlinear dynamic systems perspective, there is not any assumption regarding a supposed standard track a patient should follow. Individual trajectories should be more complex than averaged tracks, highly individual, and characterised by pattern transitions. Method: We tested if high-frequency (daily) trajectories of change are moving on standard tracks, if there are different complexity levels of high-versus low-frequency time series, if 'not on track' dynamics will be correlated with poor outcome and if complexity peaks representing the critical instabilities of a process will be correlated with the outcome. The patients included in the data analysis (N = 88) used the Therapy Process Questionnaire (TPQ) for daily self-assessments and the ICD-10based Symptom Rating (ISR) for outcome evaluation. Results: High-frequency trajectories are not running on standard tracks and are not necessarily correlated with poor outcome. Locally increased complexity may be associated with good outcome. Conclusion: It may be useful to move beyond the concept of standard tracks and expected treatment outcomes. Routine feedback procedures should use the information that is given by the nonlinear dynamics of multiple change criteria. K E Y W O R D S dynamic complexity, nonlinear dynamic systems, on track versus. not on track, processoutcome research, psychotherapy feedback This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Psychotherapy Research, 2010

The authors developed a concept that applies self-organization theory to psychodynamic principles. According to this concept, episodes of temporary destabilization represent a precondition for abrupt changes within the therapeutic process. The authors examined six courses of therapy (patients diagnosed with depression and personality disorder). After each therapy session, patients rated their experience of the therapeutic interaction. A measure of instability was used to identify episodes of destabilization with respect to patients' interaction experience throughout the process. Episodes of pronounced destabilization occurred in the four courses of therapy that showed better therapy outcomes. These episodes were characterized by temporary strong deteriorations in interaction experience (negative peaks). Three of the four courses showed subsequent discontinuous improvements to a higher level of interaction. Results indicate that the systematic inclusion of a measure of instability is worthwhile in investigations of discontinuous changes. This method allows the theoretical assumptions of the psychodynamic approach to be tested.

Frontiers in Psychology, 2020

Statistical mechanics is the field of physics focusing on the prediction of the behavior of a given system by means of statistical properties of ensembles of its microscopic elements. The authors examined the possibility of applying such an approach to psychotherapy research with the aim of investigating (a) the possibility of predicting good and poor outcomes of psychotherapy on the sole basis of the correlation pattern among their descriptors and (b) the analogies and differences between the processes of good-and poor-outcome cases. This work extends the results reported in a previous paper and is based on higher-order statistics stemming from a complex network approach. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and transcripts of the sessions were coded according to Mergenthaler's Therapeutic Cycle Model (TCM), i.e., in terms of abstract language, positive emotional language, and negative emotional language. The relative frequencies of the three vocabularies in each word-block of 150 words were investigated and compared in order to understand similarities and peculiarities between poor-outcome and good-outcome cases. Network analyses were performed by means of a cluster analysis over the sequence of TCM categories. The network analyses revealed that the linguistic patterns of the four good-outcome and four poor-outcome cases were grounded on a very similar dynamic process substantially dependent on the relative frequency of the states in which the transition started and ended ("random-walk-like behavior", adjusted R 2 = 0.729, p < 0.001). Furthermore, the psychotherapy processes revealed statistically significant changes in the relative occurrence of visited states between the beginning and the end of therapy, thus pointing to the non-stationarity of the analyzed processes. The present study showed not only how to quantitatively describe psychotherapy as a network, but also found out the main principles on which its evolution is based. The mind, from a linguistic perspective, seems to work-through psychotherapy sessions by passing from Frontiers in Psychology | www.frontiersin.org 1