The Complex Matters of the Mind

The following sections are included:

• CONTENTS

• INTRODUCTION

• FOREWORD

A definition of the objects of a science in terms of precise measuring operations M gives the objects a set-theoretical character whereby complexity, seen as a multiplicity of possible final outcomes, emerges. An adaptive strategy introduces a frequent readjustment of the M settings, which reduces that multiplicity. This way, an adaptive cognitive task can be seen as the extraction of a simple map out of a complex landscape.

The study of complex systems and Fractal Geometry is having a profound impact on the physical sciences. These concepts change the way we look at nature and permit to include in the scientific domain new phenomena characterized by strong irregularities and self-similarity. Experiments and observations indeed suggest that many physical systems develop spontaneously self-similar behavior both in space and time. Pattern formation, aggregation phenomena, biological and geological systems, disordered materials, clustering of matter in the universe are just some of the fields in which scale invariance has been observed as a common basic feature. The value and impact of these concepts, however, is still rather controversial. In this paper we discuss the main advancements as well as the present limitations of these ideas by discussing some (representative) examples. We also outline the main open problems and the possible developments of the field.

Major goals for psychoanalysis and psychology are the description, analysis, prediction, and control of behaviour. Natural language has long provided the medium for the formulation of our theoretical understanding of behavior. But with the advent of nonlinear dynamics, a new language has appeared which offers promise to provide a quantitative theory of behaviour. In this paper, some of the limitations of natural and formal languages are discussed. Several approaches to understanding the links between natural and formal languages, as applied to the study of behavior, are discussed. These include symbolic dynamics, Moore's generalized shifts, Crutchfield's ∊ machines, and dynamical automata.

Language and thinking give us access to what is usually called natural and social reality. Language and thinking can be considered as parts of a semiotic universe of different entities from which emerge different subsets. Some of these can have peculiar functions in interpersonal interactions and biological transductions. Nonlinear studies at the morphological level of language are opening new perpectives in this area of the Mind-Sciences.

This paper is a survey of a few recent contributions in which dyadic processes are studied as formal dynamical systems. For this, a general minimal model composed of two ordinary differential equations is first considered as a possible formal tool to mimic the dynamics of the feelings between two persons. The equations take into account three mechanisms of love growth and decay: the pleasure of being loved (return), the reaction to partner's appeal (instinct), and the forgetting process (oblivion). Under extremely simple assumptions on the behavior of the individuals, the minimal model turns out to be a positive linear system enjoying, as such, a number of remarkable properties, which are in agreement with common wisdom on the argument. These properties are used to explore the consequences that individual behavior can have on community structure. The main result along this line is that individual appeal is the driving force that creates order in the community. Then, in order to make the assumptions more realistic, in accordance with attachment theory, individuals are divided into secure and non secure individuals, and into synergic and non synergic individuals, for a total of four different classes. Using always the same minimal model, it is shown that couples composed of secure individuals, as well as couples composed of non synergic individuals can only have stationary modes of behavior. By contrast, couples composed of a secure and synergic individual and a non secure and non synergic individual can experience cyclic dynamics. In other words, the coexistence of insecurity and synergism in the couple is the minimum ingredient for cyclic love dynamics. Finally, a slightly more complex model, composed of three ordinary differential equations, proposed to study the dynamics of love between Petrarch, a celebrated Italian poet of the 14-th century, and Laura, a beautiful but married lady, is also reviewed. Possible extensions are mentioned at the end of the paper.

The dynamics of heartbeat interval time series were studied by a modified random walk analysis recently introduced as Detrended Fluctuation Analysis. In this analysis, the intrinsic fractal long-range power-law correlation properties of beat-to-beat fluctuations generated by the dynamical system (i.e. cardiac rhythm generator), after decomposition from extrinsic uncorrelated sources, can be quantified by the scaling exponent which, in healthy subjects, is about 1.0. The finding of a scaling coefficient of 1.0, indicating scale-invariant long-range power-law correlations (1/ƒnoise) of heartbeat fluctuations, would reflect a genuinely self-similar fractal process that typically generates fluctuations on a wide range of time scales. Lack of a characteristic time scale suggests that the neuroautonomic system underlying the control of heart rate dynamics helps prevent excessive mode-locking (error tolerance) that would restrict its functional responsiveness (plasticity) to environmental stimuli. The 1/ƒ dynamics of heartbeat interval fluctuations are unaffected by exposure to chronic hypoxia suggesting that the neuroautonomic cardiac control system is preadapted to hypoxia. Functional (hypothermia, cardiac disease) and/or structural (cardiac transplantation, early cardiac development) inactivation of neuroautonomic control is associated with the breakdown or absence of fractal complexity reflected by anticorrelated random walk-like dynamics, indicating that in these conditions the heart is unadapted to its environment.

The treatment implications of understanding mind as solely epiphenomenal to nonlinearly founded neurobiology are discussed. G. Klimovsky's epistemological understanding of psychoanalysis as a science is rejected and treatment approaches integrating W. R. Bion's and D. W. Winnicott's work are supported.

Underlying idea: A new hypothesis about how the mental state of psychosis may arise in the brain as a “linear” information processing pathology is briefly introduced. This hypothesis is proposed in the context of a complementary approach to psychiatry founded in the logical paradigm of chaos theory. To best understand the relation between chaos theory and psychiatry, the semantic structure of chaos theory is analyzed with the help of six general, and six specific, fundamental characteristics which can be directly inferred from empirical observations on chaotic systems. This enables a mathematically and physically stringent perspective on psychological phenomena which until now could only be grasped intuitively: Chaotic systems are in a general sense dynamic, intrinsically coherent, deterministic, recursive, reactive and structured: in a specific sense, self-organizing, unpredictable, nonreproducible, triadic, unstable and self-similar. To a great extent, certain concepts of chaos theory can be associated with corresponding concepts in psychiatry, psychology and psychotherapy, thus enabling an understanding of the human psyche in general as a (fractal) chaotic system and an explanation of certain mental developments, such as the course of schizophrenia, the course of psychosis and psychotherapy as chaotic processes.

General overview: A short comparison and contrast of classical and chaotic physical theory leads to four postulates and one hypothesis motivating a new, dynamic, nonlinear approach to classical, causal psychiatry: Process-Oriented PSYchiatry or “POPSY”, for short. Four aspects of the relationship between chaos theory and POPSY are discussed: (1) The first of these, namely, Identification of Chaos / Picture of Illness involves a definition of Chaos / Psychosis and a discussion of the 6 logical characteristics of each. This leads to the concept of dynamical disease (definition, characteristics and examples) and to the idea of “psychological disturbance as dynamical illness”. On the one hand, it is argued that the developmental course of psychosis is chaotic. On the other hand, we propose the hypothesis that the mental state of psychosis may be a linear information processing pathology. (2) The second aspect under discussion is the Assessment of Chaos / Diagnosis of Illness. In order to better understand how POPSY research treats this aspect, we take a look at the 3 different classes of (non-quantum) motion as models of 3 different possible courses of illness and outline present-day methods available for the quantitative assessment of chaotic (fractal) motion. (3) The third aspect, namely. Prediction of Chaos / Prognosis of Illness considers how each of these 3 classes of motion implies a different way of looking into the future: linear-causal, statistical and nonlinear-fractal, respectively (4) The fourth aspect of the relationship between chaos theory and POPSY, Control of Chaos / Treatment of Illness, is shown to have certain implications to complementary medicine. This paper completes with a short summary, conclusion and a closing remark.

This chapter surveys community process in a circumscribed area of central Italy in a comparative effort to show how simple quantitative methods can provide insights into the nature of community constitution. It is evident that individual and psychological processes are rooted in community experience, and in order to have a fuller understanding of the various system levels discussed in this volume, it is valuable also to have some insights into the organizational dynamics of localized communities.

Some critical issues about the relative portability of models and solutions across disciplinary barriers are discussed. The risks linked to the use of models and theories coming from different disciplines are evidentiated with a particular emphasis on biology. A metaphorical use of conceptual tools coming from other fields is suggested, together with the unescapable need to judge about the relative merits of a model on the basis of the amount of facts relative to the particular domain of application it explains.

Some examples of metaphorical modeling coming from biochemistry and psychobiology are briefly discussed in order to clarify the above positions.