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Author by : Xiao Yang
Languange Used : en
Release Date : 2020
Publisher by :
ISBN : OCLC:1198444979
This body of work introduces and forwards a Boolean network-based method for studying psychological dynamics, both within-person and between-persons. I outline the Boolean network method, provide a guide for implementation, and illustrate how the method is applied in two empirical settings -- study of children's self-regulation, and study of group-therapy processes. The work highlights the utility of the method for obtaining intuitive descriptions of individual or group processes and deriving strategies for directing the individual or group towards desired outcomes. Developmental science is making use of dynamical system methods to explain the mechanisms of change driving human development and to predict how and when individuals or groups will change. A natural next step is to understand how to intervene when problematic patterns or change arise. Although psychological researchers have proposed and explored use of network methods to design interventions, applications are sparse. My aim is to enrich the repertoire of methods researchers can use to learn about and direct individuals' and groups' psychological functioning, and in doing so to prompt further use of network methods for modeling behavior change. In Chapter 1, I outline the motivation for introducing a Boolean network method that can be used to describe psychological systems and design interventions that may optimize how those systems function. Although a number of researchers have outlined the possibility of using dynamical system methods to guide psychological processes to desired levels, methods for deriving control strategies have remained theoretical. In this chapter, I identify a gap in the research on methods for analysis of developmental and psychological change processes -- specifically, the sparsity of empirical applications of control system design despite its theoretical importance -- and introduce how a Boolean network control method (Kauffman, 1969; 1993) can address this gap. Second, I briefly explain why network control is useful for guiding developmental processes, and how methods at the overlap between dynamical systems methods and network analysis can be used to develop that guidance. Third, I clarify how within- and between-person dynamics are conceptualized in this project, and how the definitions used here are analogous to other terms used in psychology. Fourth, I explain why the same dynamical system method can be used to describe both within- and between-person dynamics. I then briefly outline two empirical studies where I demonstrate how the Boolean network method can be applied to study and control of both within- and between-person dynamics. In Chapter 2, I revisit how dynamical system methods are used to model the nonlinear dynamics of multivariate systems. Despite the interest and advancement of control theory to direct psychological dynamics toward desired goals, control has been less studied and rarely applied in nonlinear psychological systems. We introduce the Boolean network method to address this gap. This method is useful because it can be used to model the nonlinear dynamics in multivariate systems and to develop network control strategies that might be used to manage the system toward a desired state. The Boolean network method is a discrete-time dynamical system method, and we introduce this method in three steps: (1) inference of the temporal relations between multiple binary variables as Boolean functions and construction of Boolean networks in which the binary variables are nodes and the Boolean functions are edges, (2) extraction of attractors based on the inferred dynamics and assignment of desirability for each attractor, and (3) design of network control to direct a psychological system toward a desired attractor by identifying how the Boolean network needs to be updated. To demonstrate how the Boolean network can describe and prescribe control for emotion regulation dynamics, we applied this method to an observational dataset of children's regulation of anger using bidding and/or distraction behavior (N = 120, T = 480 seconds). Network control strategies were designed to move the child into attractors where anger is OFF. The sample shows heterogeneous emotion regulation dynamics across children in 22 distinct Boolean networks, and heterogeneous control strategies regarding which behavior to perturb and how to perturb it. The presentation and illustration forward the Boolean network method as a novel method to describe nonlinear dynamics in multivariate psychological systems and a control method to guide nonlinear psychological systems toward desired goals. In Chapter 3, I revisit theories suggesting group processes can induce desired or undesired behavior change in individuals in a group because they are under social influence. Empirical modeling of group processes often assumes the social influence is assimilative only, and network-based interventions that aim to manage group processes and promote desired behavior change does not apply when the social network is fully connected. We introduce the Boolean network method to address these two gaps because it allows both assimilative and repulsive social influence to be modeled simultaneously, and prescribes network control strategies by changing a few group members' behavior regardless of network topology. The Boolean network method is a dynamical system method that models the group-specific temporal relations between group members' behavior as a Boolean network, and also allows for control theory to design group management strategies and direct the groups toward desired behavior. The Boolean network method is applied to empirical data of individuals' self-disclosure behavior in multi-week therapy groups (N = 155, 18 groups, T = 10~16 weeks), to model and manage group-specific processes of self-disclosure. Results show the method can estimate each group member's self-disclosure with error rate of 0.14 (SD = 0.10). Both assimilative and repulsive social influence are found in 14 out of 18 groups. Group-specific network control strategies were designed to elicit the majority of the group self-disclose by encouraging a few group members' self-disclose behavior. This example illustrates the Boolean network as a flexible method that allows for modeling of assimilative and repulsive social influences that simultaneously operate in a group process and design of strategies that can be used to direct the group process to desired states (without manipulating the social ties). This dissertation introduces and forwards the Boolean network method as a method that can be used to describe and control a system's trajectory. The final chapter, Chapter 4, summarizes the contribution of this dissertation in terms of method innovation, theory, data, and potential applications, and begins to elaborate how the method might be extended further. To our knowledge, this is the first application of the Boolean network method in describing and controlling nonlinear psychological processes. The Boolean network method follows the long-standing tradition of using dynamical system methods to explain, model, and predict how complex psychological systems operate and change over time. This dissertation adds to that literature by providing the methodological steps and empirical examples that will enable control system design for nonlinear within- and between-person dynamics. Our demonstration emphasizes the appeal of this method for both theory and practice -- providing simple descriptions and explanations of system dynamics and system control strategies. Altogether, this dissertation forwards and provides access to a useful tool that can help researchers discover, understand, and shape many different kinds of psychological dynamics....