A theory and dynamic model of dyadic interaction: Concerns, appraisals, and contagiousness in a developmental context

Dynamic Systems, Contextual Influences, and Multiple Timescales: Emotion Regulation as a Resilience Resource

We need to understand how psychosocial resources develop, identify the influences that threaten their maintenance, detect the circumstances under which these resources are used, and elucidate the factors that support and promote their growth. Three important components to studying the development of resilience include its dynamic nature, context, and timescale of measurement. Dynamic systems (DS) approaches focus on physiological and psychological structures underling the development of resilience by explicitly mapping parameters of change onto their corresponding aspects of functioning. Previous research has captured emotion regulation within individuals, across traits, and in close personal relationships to show how these methods depict dynamic regulation/resilience resources and their influence on outcomes of interest. The use of multi-time scaled data informs how daily emotion regulation is disrupted in the context of stress to produce dysregulation and disease later in the life course. This approach can also reveal how resilience resources counteract these adverse processes and allow others to thrive and be well. Researchers must not only explore short-term variation in constructs of interest, but also explore how these shorter-term fluctuations contribute to longer-term changes. The confluence of DS, contextual influences, and multiple timescales provides an important set of tools to better understand development.

Development of second-order theory of mind: Assessment of environmental influences using a dynamic system approach

Theory of mind refers to the ability to attribute beliefs to oneself and others. The present study used a dynamic systems approach to assess how environment may affect the development of second-order theory of mind (e.g., “John knows that Mary knows that he went out yesterday”). Theory of mind is divided into two major dimensions: comprehension (i.e., to understand a mental state) and prediction (i.e., to predict someone else’s future behavior or mental state). Two age groups were assessed: 5–6- and 10–11-year-old children. In both age groups, participants were assigned to a condition of “support” (help provided) or “no support” (help not provided). Results show that second-order theory of mind follows a dynamic growth law that depends on support. Support facilitates performance in theory of mind production (i.e., to predict one’s future behavior) for both the 5–6- and 10–11-year-old children. Interestingly, the 5–6-year-olds who received support presented an increase in the second-order prediction performance at the expense of the second-order comprehension, suggesting that a temporary dip in comprehension performance may facilitate the development of mental rules to predict one’s future behavior.

Explaining the “How” of Self-Esteem Development: The Self-Organizing Self-Esteem Model

The current article proposes a theoretical model of self-esteem called the Self-Organizing Self-Esteem (SOSE) model. The model provides an integrative framework for conceptualizing and understanding the intrinsic dynamics of self-esteem and the role of the context across 3 levels of development: The macro level, which is the level of trait self-esteem, the meso level, on which we find state self-esteem, and the micro level, which is the level of discrete self experiences. The model applies principles from the complex dynamics systems perspective to self-esteem, and can thus uniquely describe the underlying mechanism of self-esteem development based on self-organizational processes and interacting time scales. We compare the proposed SOSE model with a formalized account of the traditional approach to self-esteem, showing that the SOSE model is especially conducive to the understanding of self-esteem development in a way that the traditional approach is not—namely, in its ability to explain and predict the underlying dynamics of trait and state self-esteem, the meaning of variability, and the role of the context.

Dynamical systems modeling of physiological coregulation in dyadic interactions

In this paper, we used a dynamical systems approach to examine the interrelations of the physiological signals in dyadic interactions. We introduced a system of differential equations developed for dyadic research (Felmlee and Greenberg, 1999) and applied it to time series of heart rate and respiration from individuals in 32 couples. The model included parameters representing self-regulation and coregulation for each individual in the dyad. We applied the model directly to each dyad's data and then examined the distributions of the parameters, and compared such parameters across the three laboratory tasks. In the final step, we explored the associations between the parameter estimates extracted from the physiological data with those parameters from a similar model fitted to daily self-reports of affect.

Networks as complex dynamic systems: applications to clinical and developmental psychology and psychopathology

Cramer et al.'s article is an example of the fruitful application of complex dynamic systems theory. We extend their approach with examples from our own work on development and developmental psychopathology and address three issues: (1) the level of aggregation of the network, (2) the required research methodology, and (3) the clinical and educational application of dynamic network thinking.