Understanding Psychological Phenomena as Complex Systems

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Symposium at the Conference on Complex Systems 2018

  • Time: Thursday 27th, 9.00 -13.00
  • Location: Room 10
  • Organizer: Jonas Haslbeck

Schedule

9.00 – 9.30 Adela Isvoranu – Towards a dynamical systems approach to mental disorders [Slides]
9.30 – 10.00 Giulio Costantini – Psychological dynamics in different classes of individuals [Slides]
10.00 – 10.30 Oisin Ryan – Challenges in characterizing psychopathologies as “unhealthy” dynamic systems [Slides]
10.30 – 11.00 Break
11.00 – 11.30 Jonas Haslbeck – A Dynamical Model of Panic Disorder [Slides]
11.30 – 12.00 Gaby Lunansky – Bouncing back from adversity: Towards a dynamical conceptualization of psychological resilience [Slides]
12.00 – 12.30 Jonas Dalege – Psych(w/o)metrics: A Quantum Mechanics Analogy of Psychological Measurement [Slides]
12.30 – 13.00 Panel Discussion

Description

Psychological phenomena are recognized as complex in that they are determined by a potentially huge number of variables that are interacting with each other non-linearly. Whereas traditionally psychological research has focused on verbal theories and statistical modeling of data, in recent years psychological phenomena have been increasingly approached from a complex systems perspective. This change is driven by new theoretical considerations: for instance, in clinical psychology, mental disorders have been proposed to emerge from interactions between symptoms rather than being caused by an unobserved “disease variable”. A second reason for the surge of interest in complex systems is the increasing availability of time-series measurements of individual persons using sensors and experience sampling methods. These data provide the basis for validating dynamic models empirically that has been missing so far.

In this symposium we use ideas from complex systems research to approach psychological phenomena from two sides: the first three presentations use the conceptual ideas behind complex/dynamical systems to motivate new statistical tools to analyze psychological data. Adela Isvoranu will give an introduction to network models in the field of psychology, Giulio Costantini will introduce a new type of network model that allows to abstract the complex interactions between psychological variables for different classes of individuals, and Oisín Ryan will critically assess efforts to identify network structures that are associated with mental disorders.

After the break, we will use complex systems ideas to approach psychological phenomena from a purely theoretical side: Jonas Haslbeck will introduce a dynamical model of panic disorder, Gaby Lunansky will discuss the concept of psychological resilience from a dynamical systems perspective and Jonas Dalege will introduce quantum physics as an analogy for psychological measurements to question traditional views on the psychological measurement process. The last 30 minutes are reserved for a panel discussion in which we hope to discuss how to inform theory with available empirical data and future directions in studying psychological phenomena from a complex systems perspective.

Abstracts

Adela Isvoranu

  • University of Amsterdam, Department of Psychological Methods
  • Title: Towards a dynamical systems approach to mental disorders

Recent years have seen a rise in the modeling of mental disorders as networks of interacting symptoms. The centerpiece of network modeling lies in the idea that mental disorders emerge from complex interactions between psychological, biological and sociological components, and that symptoms are active causal agents in producing disorder states. The patterns of interaction can be visualized in a network structure, in which variables (e.g., symptoms, environmental factors, genetic factors) are represented as nodes and the presence of an edge between any two nodes implies the existence of a statistical association. This presentation aims to provide a general introduction to the network approach to mental disorders and provide several examples of network models as constructed – from data – in the field of psycho(patho)logy.

Giulio Costantini

  • University of Milan-Bicocca, Department of Psychology
  • Title: Psychological dynamics in different classes of individuals

Several complex psychological phenomena, including for instance psychopathology and healthy personality emerge from interactions among lower-level phenomena such as cognitions, behaviors, emotions, motivations, and symptoms. Psychologists are often interested to examine whether and how the dynamics underlying different classes of individuals, such as patients and controls, are similar or different. The Gaussian Graphical Model (GGM) has been used as a parsimonious representation of between-person and within-person dynamics in psychology. GGMs are typically estimated from psychological data using the graphical lasso algorithm, which does not accommodate for multiple classes of individuals. The Fused Graphical Lasso can be used to jointly estimate GGMs in different classes and to identify similarities and differences in their underlying dynamics. We present applications of FGL to several psychological domains, including personality, situation perception, social interactions, and psychological disorders such as the post-traumatic stress disorder, pathological narcissism, and the borderline personality disorder. We discuss the most important insights afforded by this methodology for the dynamics underlying these phenomena.

Oisín Ryan

  • Utrecht University, Department of Methodology & Statistics
  • Title: Challenges in characterizing psychopathologies as “unhealthy” dynamic systems

Recent years have seen a surge of interest in treating psychological disorders as dynamical systems, conceptualized as networks of interacting components. This approach is appealing in that it places the focus of attention on causal interactions between the components of a disorder, promising a mechanistic account of the presence or absence of pathology. Additionally, complex systems concepts such as hysteresis, critical transitions and bi-stable systems appear to have a promising mapping to psychological theories on disorders, e.g. persistent depressive symptomatology, manic-depressive disorder.

Most of the theoretical work on this topic focuses on bi-stable network models, in which nodes represent symptom variables. While much of the empirical work on mental is motivated by this theoretical work, it deviates from it in important ways: first, the modeled variables are not only symptoms, but a large variety of psychological constructs (e.g. emotions). And second, one typically estimates simple (linear) models with only a single fix point.

This talk will present a critical examination of the current challenges of the network approach to psychopathology, focusing on the problems following from treating different scenarios (network components, model types) as interchangeable in the search for characterizations of psychopathology in the structure of the network. We identify areas in the literature which need to be more thoroughly fleshed out to allow for a fruitful mapping between theoretical psychological ideas, dynamic systems concepts, and empirical research.

Jonas Haslbeck

  • University of Amsterdam, Department of Psychological Methods
  • Title: A Dynamical Model of Panic Disorder

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The network theory of psychopathology posits that mental disorders can be conceptualized and studied as complex systems of mutually reinforcing symptoms. We extend this general framework by developing a network theory for a specific mental disorder: Panic Disorder. We first review prior theory and research on the phenomenology of Panic Disorder to identify its core components and the relations among them. We then use this prior work to construct and evaluate a dynamical systems model of Panic Disorder. We show that this model is able to account for key phenomenological characteristics of recurrent panic attacks as well as the onset and treatment of Panic Disorder. Just as importantly, the model identifies gaps in our understanding of Panic Disorder, thereby identifying critical areas for future research and theory development. We conclude with a discussion the implications of the model for our understanding of the nature and treatment of mental disorders.

Gaby Lunansky

  • University of Amsterdam, Department of Psychological Methods
  • Title: Bouncing back from adversity: Towards a dynamical conceptualization of psychological resilience

The question of why some people develop psychopathology following adversity while others do not, has been extensively investigated in the psychological literature. A pressing yet unanswered question is what makes the latter group of people resilient in that they are seemingly able to withstand the ‘attack’ that adverse events launch on their health and well-being. Research seems to be predominantly about rather static indicators (e.g., childhood maltreatment) while the concept of resilience is deeply dynamical. Moreover, while other theoretical definitions sound dynamic, quite a few operationalizations are static in nature. To date, there is no consensus on what resilience exactly is conceptually, which seriously hampers progress as various definitions and operationalizations abound. A relatively new modeling approach conceptualizes mental disorders as a dynamical system in which the dynamics are driven by a network that consists of symptoms of a disorder that stand in certain direct relations to one another. Based on such a dynamical systems perspective, this presentation will discuss an alternative framework of psychological resilience. It will be shown how the architecture of psychopathological networks can be related to resilience indicators (e.g., connectivity of the network is related to robustness). This opens the door to a new conceptualization of resilience in the psychological field, by applying ideas from dynamical systems theory onto the concept of resilience, studying the dynamics of the new individual network models.

Jonas Dalege

  • University of Amsterdam, Department of Psychological Methods
  • Title: Psych(w/o)metrics: A Quantum Mechanics Analogy of Psychological Measurement

The main principle of our recently proposed Attitudinal Entropy framework holds that the main function of human thought is to reduce the entropy of cognitive representations. This principle has fundamental implications for psychological measurement because it creates a situation that is similar to the measurement problem in quantum mechanics – measurement is not possible without disturbing the system. Similar to the collapse of the wave function in quantum mechanics, psychological measurement causes individuals to pay attention and think about the measurement, implying that the measured construct is in fact created by the measurement (similar to the consequence of measuring a particle creates a definite state of the particle). Consequences of this implication include that (a) the usual definition of measurement error in psychometrics becomes meaningless because psychological constructs are not entities that exist independently of measurement, (b) the question of whether psychological constructs are real or not becomes an ill-posed question, (c) only measurements that have real-world analogues provide meaningful information of psychological processes, and (d) psychological measurement is inherently dynamic and not static.