About this Research Topic

Background

Psychotherapeutic and educational encounters are complex systems in their own right. Last century, the prevailing scientific models based on the idea of objectivity and a universe composed of isolated
objects subject to laws of linear causality, devalued the complexity of reality. Currently, the study of complex systems transcends the mechanistic and reductionist methods for describing linear processes and requires suitable approaches to describe probabilistic and scarcely predictable phenomena. Since the third millennium, other research methods have become the focus of scholars: the theory of complex networks, neural networks for deep learning, decision trees and cluster analysis methods. These methods were born with the aim of simulating the activity of neuronal networks and have shown great utility as descriptive means of natural phenomena. They have given a great impulse to the field of artificial intelligence research, opening the way to research fields such as computational psychology, psychopathology and more recently, computational psychotherapy.

With this Research Topic, we aim at overcoming this divide by inviting theoretical and empirical contributions targeting the complex approach to psychotherapy and psychopathology. We suggest that using a complex network to study the interaction between the parts of the patient-therapist system can elucidate both the tendency of the system to develop an internal order, thus to be self-organizing and autopoietic, and the manner through which self-organization is produced. A quantitative understanding of the network requires treating it as an abstract object by means of mathematical tools that describe the structure of the interaction, i.e., the arrangement of connections between elements; the behavior of the system is inferred
from the interaction of the behaviors of the individual elements.
This complex perspective can gain new insights into the fields of psychotherapy, psychopathology, and pedagogy and open new avenues for interdisciplinary research in these areas.

Contributions are welcome that focus on projects, regardless of epistemological orientation, that are able to demonstrate the effectiveness of therapeutic and educational processes, as well as their evaluation, in the perspective of complexity and can cross disciplinary boundaries.

Potential contributions may be:

1. Discuss the various methods and tools for an integrative framework of the complex approach to research in psychotherapy, psychopathology and pedagogy;

2. Research to measure the type and form of the relationship of the patient-therapist system to evaluate both the general structure and the dynamics of the specific complex organization that is a therapy session;

3. Empirical research to bring out from the description of the structure and dynamics of the therapeutic and learning relationship, hypotheses about the ingredients that determine the outcome of the processes, to be subjected to empirical verification;

4. Target conceptual challenges (e.g. different notions of complex systems and complex approach to the research in psychotherapy and psychopathology, and development across disciplines), technological obstacles (data integration, big data, and neuroscience), and practical applications (e.g. psychotherapy and psychiatric practices, psychopathological assessment, pedagogy, education, robotics).

Keywords: Psychotherapy, Psychopathology, Pedagogy, Education, Neural Networks, Patient-therapist relationship, Complex systems, Graph Theory, Machine Learning

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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