What Differentiates Poor and Good Outcome Psychotherapy? A Statistical-Mechanics-Inspired Approach to Psychotherapy Research

Giulio Felice,Alessandro Giuliani,Gloria Lagetto,Silvia Andreassi,Gabriele Serafini,Giulia Paoloni,Andrea Scozzari,Nicoletta Vegni,Franco Orsucci,Omar Gelo,Erhard Mergenthaler

doi:10.3390/systems7020022

Abstract

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. “Static analyses” (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the “dynamic analyses”, based on five coarse-grained descriptors related to variability, the degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature enough to shift from a metaphorical to a fully quantitative status.

Highlights

In the literature, there have been many examples aimed at finding coarse-grained descriptors able to explain the behavior of complex systems composed of several different elements
The PCA confirmed this observation displaying a flat eigenvalues distribution (Table 1), so pointing to the lack of a relevant shared correlation structure at this level of analysis. This implies that the adopted coding system is made of largely independent categories; in other words, abstract language (AB), POS, and NEG
The most evident difference was linked to the patient’s use of abstract language, interpreted very positively in poor-outcome cases and very negatively in good-outcome cases. This observation is closely associated to the use of positive and negative emotional languages inversely proportional to abstraction only in poor-outcome patients

Summary

Introduction

There have been many examples aimed at finding coarse-grained descriptors able to explain the behavior of complex systems composed of several different elements. Statistical thermodynamics has emphasized the importance of focusing on the dynamics of the degree of order of a system [1] This approach can be extended to any scientific field, posited that we get a sensible measure of system autocorrelation [2,3,4,5]. Many studies [6,7,8] showed the usefulness of looking at biological systems from the perspective of statistical mechanics, that is, focusing on the mutual correlations among system descriptors. This scientific stance takes the name of “middle-out” approach since it focuses on a mesoscopic level maximizing the correlations among system descriptors. This approach lies “in the middle” between pure “bottom-up” (the causally relevant layer is the microscopic one) and “top-down” (the causally relevant layer is where general laws are defined) approaches [9,10]

Methods

Results

Conclusion