Strunk Guido, Lichtwarck-Aschoff Anna
Complexity-Research, Schönbrunner Str. 32 / 20, A-1050 Vienna, Austria.
Department of Entrepreneurship and Economic Education, Faculty of Business and Economics, Technical University Dortmund, Germany.
J Pers Oriented Res. 2019 Dec 30;5(2):81-100. doi: 10.17505/jpor.2019.08. eCollection 2019.
The conventional view on interventions as mechanistically causing interchangeable clients to get better has come under attack. Group-based and linear approaches fall short in adequately describing the idiosyncratic and dynamic nature of treatment processes. Non-linear dynamic system theories in contrast hold great potential to better conceptualize and understand the generalities and idiosyncrasies of psychotherapeutic change processes. The aim of this study was to examine whether we can detect markers of complex dynamical systems behavior in two single-case therapies. All sessions from both therapies were coded with sequential plan analysis using a 10s sampling frequency. The coding system incorporates verbal and non-verbal behaviors and allows for the representation of contextualized interactive behaviors. The high sampling frequency results in long time series, which allowed us to apply non-linear analysis techniques. We found strong support for complex behavior and the existence of a butterfly effect, i.e., a relatively short prediction horizon in which reliable predictions about the system's future behavior could be made. Further, critical fluctuations as a marker for phase-transitions were detected that were accompanied with different interactional patterns in both therapies. Finally, there was strong support for self-organized pattern formation, with a few interactional patterns dominating the interaction. Considering that we are intervening on complex dynamical systems means that we have to (1) acknowledge the principal individuality of change processes, (2) accept the fundamental limitations of the mechanistic input-output model of treatment effects and (3) appreciate the impossibility of long-term predictions of treatment responses.
传统观点认为干预措施能机械地使可互换的患者病情好转，这种观点已受到抨击。基于群体和线性的方法在充分描述治疗过程的独特性和动态性方面存在不足。相比之下，非线性动态系统理论在更好地概念化和理解心理治疗变化过程的一般性和特殊性方面具有巨大潜力。本研究的目的是检验我们是否能在两种单病例治疗中检测到复杂动力系统行为的标志物。使用10秒采样频率，通过序列计划分析对两种治疗的所有疗程进行编码。编码系统纳入了言语和非言语行为，并允许呈现情境化的互动行为。高采样频率产生了长时间序列，这使我们能够应用非线性分析技术。我们发现了对复杂行为和蝴蝶效应存在的有力支持，即存在一个相对较短的预测期，在这个预测期内可以对系统的未来行为做出可靠预测。此外，还检测到作为相变标志物的临界波动，两种治疗中都伴随着不同的互动模式。最后，对自组织模式形成有有力支持，少数互动模式主导着互动。考虑到我们正在对复杂动力系统进行干预，这意味着我们必须：（1）承认变化过程的主要个体性；（2）接受治疗效果机械输入-输出模型的根本局限性；（3）认识到对治疗反应进行长期预测的不可能性。
