To begin with caveats and limitations, we proposed that self-organization processes should be regarded as a systems-theoretical background for self-actualization pro- cesses that Maslow put forward as growth motivation. Yet, the two processes, self- organization and self-actualization, are not strictly analogous—note that the“self”in self-organization is not identical to the psychological self in self-actualization (Tschacher & Rössler, 1996). We may also doubt that the generation of self- organized attractors is necessarily always beneficial as suggested in self-actualiza- tion—new patterns may not be the welcomed “good” patterns that humanistic psychotherapy rather assumes. Self-organization does not follow any in-built ethical rationale, it is just the process by which systems adapt to their affordant contexts.
Therefore contextual interventions may also entail negative outcomes. There is a growing discussion on adverse side effects of psychotherapy (Linden,2013), and there is no reason to believe that contextual interventions are in principle excluded from the risk of generating adverse effects.
A further limitation arises from the kind of data available in psychology. Mental processes and experiences can be directly observed from thefirst-person perspective, but there is no simple way to transform private experiential material to third-person objective datasets, on which we however rely when applying the mathematical tools we have been developing throughout this book. This is of course a fundamental problem of psychology, but it is especially bothersome when we follow the ambition of describing the short time scales that are linked with processes of consciousness.
We have to resort to objective behavioral and physiological measures, and have to be well aware of the fact that we then depend on operationalizations. The mind cannot be measured directly, all we have at hand are bridging assumptions and the resulting correlates.
We have, in the course of modeling, inserted a number of premises to allow the models to produce what we know or assume is true—for instance, that therapists can change clients’ states. Therefore we modeled the therapeutic alliance not as a symmetrical relationship but chose the coupling terms and constants of the minimal model in a way that the therapeutic effect on the client can be larger than the client’s reverse effect on the therapist. This may be considered a limitation of our models as it rules out the, unfortunate but not completely unrealistic, treatment effect of a changed therapist and an unaltered client.
We believe nevertheless that the chances of our modeling approach outweigh the limitations. The Fokker–Planck model of psychotherapy distinguishes deterministic and stochastic impacts on clients and therapists—causation and chance—and when complemented by the slaving principle it additionally describes processes of cou- pling and entrainment, which is the working principle of self-organization. Thus we can model a large palette of change processes, which is exactly what is needed to encompass all the heterogeneous intervention methods that make up the field of psychotherapy. In saying this, we do not believe norfind that there is merely one road to Rome in psychotherapy. Psychotherapy is not an exclusively specific and deterministic enterprise, as a behavior therapist may claim, nor exclusively unspecific and contextual, as the supporters of the common factors approach say.
Various types of impacts all play their roles, deterministic, stochastic, and contex- tual, and they may even become effective during one and the same therapy course, but at different time points.
The two-dimensional minimal model showed that oscillatory dynamics and limit cycle attractors may govern the therapist–client relationship, where the client follows the therapist with a certain lag or phase shift. This model offers a new conceptual link for current empirical research on nonverbal synchrony. It may especially prove valuable when the coupling of oscillatory behaviors (such as respiration: Varga &
Heck, 2017, or periodic endocrine concentrations: Granada & Herzel, 2009) is concerned.
In formulating a minimal model of psychotherapy in the one-dimensional case, we found that an efficient“Archimedean”therapist exerts his or her influence on the client’s state by his or her personality. This archetypal therapist is a stable and slow person, which is consistent withfindings on the function of resilience and mindful- ness as therapist variables. The ideal working alliance is when the therapist remains
164 10 Outlook
independent of the client, whereas the client is strongly coupled to the therapist. This amounts to a new reading of the originally psychoanalytic concept of abstinence.
Abstinence in our view is not to mean that the therapist must be opaque to the client, but rather that the client must be able to (subconsciously?) perceive the therapist’s state and personality. Transference must be larger than counter-transference because of the necessary asymmetry of therapist–client coupling. We thus clearly assume that we deal with agentic and self-efficacious therapists who are free and competent to choose their states on their own.
The theoretical and mathematical models we have developed in this book have allowed putting forward a number of novel tools for analyzing empirical data (Chap. 9). Using these applications, researchers of psychotherapy are enabled to precisely specify the deterministic and stochastic ingredients of therapies under study. Where in one- and two-dimensional state space can we detect sources of causation and of chance? We can now localize the regions of stability, i.e., the attractors in the potential landscapes and in the synchronization patterns of a single psychotherapy course. All thesefindings can be generalized—they are not restricted to the single time series because, by a subsequent aggregation step, samples of cases can be considered. Taken together, this will put psychotherapy research in a position to answer fundamental questions regarding the process of intervention. Which kinds of effects are entailed by interventions, and how do these interventions modify the deterministic and stochastic profiles in the state space of clients and therapists? Such research can reveal which interventions have which effects—specific interventions and common factors can accordingly be classified to entail deterministic effects (generating and stabilizing attractors in state space), boundary regulation effects (reducing stochastic profiles or destabilizing attractors), or contextual effects (gen- erating new patterns in the attractor landscape).
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Index
A
Abstinence, 165 Affordance, 86, 87, 89 Antagonism, 37, 39 Archimedean role, 157 Attractor
DST, 16
dynamical systems theory, 17 stable states, 2
strange, 7
therapist-client synchrony, 2 Attractor basin, 60
Attractor landscape, 82–89, 91 Attractors, 122, 123, 125, 133, 139, 142
deterministic shift, 54 disorders, 27 effects, 21 fixed-point, 27
Gaussian distributions, 53 mean values, 55 schizophrenia, 27 shrinkage, 48, 53 stability, 29 Autoregressive (AR), 7
B
Beck’s depression inventory (BDI), 82 Behavior therapy (BT), 39, 60, 62, 68, 69 Bénard system, 65, 66
Bias, 15
Big data aspect, 11 Boundary regulation, 72–74
C
Chance, psychotherapy,seePsychotherapy Client’s consciousness, 154
Cognitive-behavioral therapy (CBT), 62 Common factors
definition, 32 types of, 32
Concordance index (CI), 129, 130, 132, 150 Constructivism, 78
Contextual interventions, 1, 39, 77, 78 Contextual model, 39
Coupling term, 94, 95
Cross-correlation function (CCF), 108–110
D Depression
bipolar, 27, 28 high-level taxa, 25 time series analyses, 28 Depressive episode, 22, 23, 82, 88 Deterministic dynamics
FPE, 54 shrinkage, 48
Diagnostic and statistical manual of mental disorders (DSM)
characteristics, 22 definition, 22 groups disorders, 23 HiTOP, 26
interval-scaled variables, 28 top-down approach, 25 Diffusion, 50, 71, 73, 78
©Springer Nature Switzerland AG 2019
W. Tschacher, H. Haken,The Process of Psychotherapy, https://doi.org/10.1007/978-3-030-12748-0
169
Dimensional view, 26, 28 Dual-aspect theory, 15
Dynamical systems theory (DST), 16, 17, 27
E
Electrocardiograms, 136–138 Electroencephalograms (EEG), 7 Empirical time series
deterministic and stochastic signatures, 149 one-dimensional FPE, 123–126
psychotherapy process, 121 two-dimensional FPE, 126, 128–130 Expansion, 50
F
Family therapy, 67, 68 Fluctuation, 71–73, 76
Fokker-Planck equation (FPE), 5, 6, 8–10, 59, 157, 158, 160, 164
body motion, 131, 132 deterministic processes
dynamics, 45
mechanical friction forces, 44 parabola, 44
probability distribution, 47, 48 psychological variable, 45 shrinkage, 48
electrocardiograms, 136–138
empirical time series, 123–126, 128–130 experiment/observation, 54
Gaussian distribution, 54 curves, 42
displacement, 42, 43
joint action, deterministic/stochastic processes, 52, 53
Markov process, 135 mathematical treatment, 104 mean values, 55–57 one-dimensional case, 41, 93 parameters, 54
psychotherapeutic interventions, 71, 72 respiration time series, 132–134 significance, 53, 54
stochastic processes, 134, 136 discrete/continuous variables, 51 expansion, 50
psychological variables, 50 two-dimensional, 98, 115, 117, 118 Friston’s free-energy principle, 160
G
Gaussian normal distribution, 13 Grawe’s common factor, 161
H
Heisenberg’s uncertainly principle, 14 Hierarchical Taxonomy of Psychopathology
(HiTOP) dimensional view, 26
hierarchical bottom-up system, 25, 26 novel classification approach, 26
Humanistic-experiential psychotherapy (HEP), 63–65, 68, 69
Humanistic psychotherapy, 67, 161
I
International classification of diseases (ICD) depressive episode, 22
groups disorders, 22 HiTOP, 26 taxonomies, 28 top-down approach, 25 WHO, 22
Interval-scaled variables, 13, 25, 28 Intervention, 60–65, 67, 69 Interventions in psychotherapy
hierarchical model antagonism, 37 taxonomy, 38 research, 31–35 therapist effects, 36–37
K
Kalmanfilter, 8
Kurt Lewin’s concept of“valence”, 161
L
Langevin equation, 8 Limit cycle, 100, 104
M
Magnetic resonance tomography (MRT), 108 Markov process, 7, 135, 139, 140, 149 Master equations
continuous variable, 51 discrete variable, 47
stochastic influences, 51 time interval, 46 Mathematical models
emotions/thoughts, 3 linear regression, 5 narrative data, 4
tautological transformations, 4 tradition, 4
type description, 3 Mathematics, 1
Mechanical friction forces, 44 Meditation, 73
Mindfulness, 33, 37
Minimal model, 94, 96, 99, 100, 103, 104, 107, 109, 110, 113, 115
Minimal model of dyadic psychotherapy, 2 Monte Carlo bootstrapping, 109
Motion energy analysis (MEA), 11, 108 Movement synchrony, 127
N
Near-infrared spectroscopy (NIRS), 108 Neo-behaviorist modeling, 4
Neurolinguistic programming (NLP), 155 Nonverbal synchrony, 127
O
Operationalizations assumptions, 4
mathematical tautologies, 4 qualitative processes, 6 semantics, 4
P
Phase shift, 104, 106, 110, 111, 113, 115 Philosophical and theoretical-psychological
convictions, 31 Physiological synchrony, 129 Positive affect, 45, 50 Potential energy, 44 Potential function, 45 Probability distribution
expansion, 50 Gaussian, 47, 53 shrinkage, 48, 49, 51 Psychiatry
disorders, 22 DSM/ICD, 22
medical sub-discipline, 24 personality profile, 23 RDoC, 24
signs and symptoms, 27
Psychoanalysis (Psa), 31, 35, 63, 64 Psychological measurement
big data aspect, 11 MEA, 11, 12 questionnaire, 9, 10 social phobia, 11 symbols and numbers, 10 variables, 9
Psychopathology
Beck’s depression inventory, 81 client with depression, 82–86, 88 common factors, 87
conceptualizations, 24–26 depression symptoms, 81, 87
deterministic antidepressive techniques, 86 deterministic intervention, 84
dynamical quantification, 26–28 Fokker-Planck equation, 81 histogram raw data, 82 stochastic destabilization, 85 stochastic inputs, 84 synergetics, 86 taxonomies, 22–23
theories and classifications, 81 therapeutic intervention, 83 Psychotherapeutic change
modeling interventions, 89–92
stochastic, deterministic and contextual, 88, 89 Psychotherapeutic intervention, 31, 38, 39, 87 Psychotherapy
affordances, 161–163 approaches, 10 AR/Markov, 7
artificial neural networks, 8, 9 causation
DST, 16
mechanical interpretations, 17 therapy success, 16
caveats and limitations, 163–165 chance
mechanical metaphor, 17 mirror symmetry, 18 phenomenological angle, 17 self-organization, 18 Chaos-theoretical, 7 common factors, 32, 33 description, 1–3, 6
deterministic interventions, 69 dynamics, 153–155
embodiment, 159, 160 Fourier transformation, 7 FPE, 6
free-energy principle, 160 (see also Interventions in psychotherapy) Kalmanfilter, 8
Index 171