Dynamical Systems Perspective Research Articles

A complex dynamic systems perspective on identity and its development: The dynamic systems model of role identity.

Current prominent models of identity face challenges in bridging across divergent perspectives and apparent dichotomies such as personal or social-collective, conscious or unconscious, and epigenetic or discursive-relational, and affording pursuit of research questions that allows integrative answers. This article presents a coherent theoretical perspective on the integrative nature of identity and its developmental mechanisms. Adopting the contextual social role as a primary unit of analysis, the Dynamic Systems Model of Role Identity (DSMRI) conceptualizes role identity as a Complex Dynamic System (CDS) anchored in action that comprises the actor's ontological and epistemological beliefs, purpose and goals, self-perceptions and self-definitions, and perceived action possibilities in the role. These system components are conceptualized as interdependent, and identity development is viewed as emergent, continuous, nonlinear, contextualized, and given to influences from within and without the system. The role identity itself constitutes an element within a multilevel hierarchy, which at the unit of analysis of the individual reflects a CDS of the multiple roles that constitute the person's psychosocial identity. Identity development involves the formation and restructuring of relations within and among role identities through intra- and interpersonal processes that are mediated by sociocognitive and cultural means, and framed by the context as well as by implicit dispositions. The DSMRI provides a framework to conceptualize and investigate the nature of the identity system, its development, and the relationship between identity development and psychological functioning at different units of-analysis, across different developmental stages and contexts, and using quantitative and qualitative methodologies. (PsycINFO Database Record

Learning infectious disease epidemiology in a modern framework.

Modern infectious disease epidemiology makes heavy use of computational model–based approaches and a dynamical systems perspective. The importance of analyzing infectious diseases in such a way keeps increasing. However, infectious disease epidemiology is still often taught mainly from a medical and classical epidemiological study design (e.g., cohort, case-control) perspective. While textbooks and other resources that teach a model-based approach to infectious diseases exist, almost any such teaching material requires students to work with mathematical models and write computer code. This is a significant barrier for students who do not have a strong mathematical background or prior coding experience, which applies to many students in public health and related biomedical disciplines. It limits the number of students who can or want to engage with infectious disease epidemiology by using modern, systems modeling–based approaches. New tools and approaches are needed to reach a wider audience and allow students to learn concepts such as the reproductive number, herd immunity, critical community size, and the population-level impact of interventions from a dynamical systems and model perspective, without the obstacles of coding or having to formulate and analyze differential equations. Here, I describe a new software package for the widely used R language that allows individuals to explore and study concepts of infectious disease epidemiology by using a modern, dynamical systems model framework, without the need to read or write computer code. The package includes documentation and material to serve as a stand-alone tool—supplemented as needed with provided references—for students to get an introduction to important modern infectious disease concepts. The package is built in a modular way that allows a student to seamlessly continue on their journey of learning infectious disease modeling if they choose to do so. The different ways to use the package are described in detail, and examples are provided.

Standing or swaying to the beat: Discrete auditory rhythms entrain stance and promote postural coordination stability

Humans seem to take social and behavioral advantages of entraining themselves with discrete auditory rhythms (e.g., dancing, communicating). We investigated the benefits of such an entrainment on posture during standing (spontaneous entrainment) and during a whole-body swaying task (intentional synchronization). We first evaluated how body sway was entrained by different auditory metronome frequencies (0.25, 0.5, and 1.0Hz). We then assessed the stabilizing role of auditory rhythms on postural control, characterized in a dynamical systems perspective by informational anchoring of the head (local stabilization) and fewer transitions from in-phase to anti-phase ankle-hip coordination (global stabilization). Our results revealed in both situations an entrainment of postural movements by external rhythms. This entrainment tended to be more effective when the metronome frequency (0.25Hz) was close to the dominant sway frequency. Particularly, we found during intentional synchronization that head movements were less variable when paced by a slower beat (informational anchoring), and that phase transitions between the two stable patterns in postural dynamics were delayed. Our findings demonstrate that human bipedal posture can be actively or spontaneously modulated by an external discrete auditory rhythm, which might be exploited for the purpose of learning and rehabilitation.

Correlations Between Hysteretic Categorical and Continuous Judgments of Perceptual Stimuli Supporting a Unified Dynamical Systems Approach to Perception.

We report from two variants of a figure-ground experiment that is known in the literature to involve a bistable perceptual domain. The first variant was conducted as a two-alternative forced-choice experiment and in doing so tested participants on a categorical measurement scale. The second variant involved a Likert scale measure that was considered to represent a continuous measurement scale. The two variants were conducted as a single within-subjects experiment. Measures of bistability operationalized in terms of hysteresis size scores showed significant positive correlations across the two response conditions. The experimental findings are consistent with a dualistic interpretation of self-organizing perceptual systems when they are described on a macrolevel by means of so-called amplitude equations. This is explicitly demonstrated for a Lotka-Volterra-Haken amplitude equation model of task-related brain activity. As a by-product, the proposed dynamical systems perspective also sheds new light on the anchoring problem of producing numerical, continuous judgments.

Dynamic characterization of a single phase square natural circulation loop

Natural circulation loop (NCL) has many applications in the energy sector as a passive heat transfer system. The passive nature of its operation increases the reliability of natural circulation loops and makes them crucial for the safety of the system. However, depending on the operating conditions, the dynamics of the system can vary significantly. For this reason proper analysis of the fluid flow dynamics in the NCL is very important. In this paper instability associated with the loop fluid flow in the NCL is investigated from a dynamical systems perspective with the help of phase plot, Fast Fourier Transform (FFT), correlation dimension and largest Lyapunov exponent. From the numerical analyses it is found that with the increase in heater power, dynamics of the fluid flow in the loop changes significantly. Loop characteristics remain steady up to a certain heater power level but with the increase in heater power level first it shows periodic oscillations and then it shows chaotic behavior. The existence of periodic and chaotic regimes is established using tools of dynamic systems analysis.

Predictors of Emerging Suicide Death Among Military Personnel on Social Media Networks.

Suicide is a leading cause of death in the United States and is the second leading cause of death in the U.S. military. Previous research suggests that data obtained from social media networks may provide important clues for identifying at-risk individuals. To test this possibility, the social media profiles from 315 military personnel who died by suicide (n=157) or other causes (n=158) were coded for the presence of stressful life situations (i.e., triggers), somatic complaints or health issues (i.e., physical), maladaptive or avoidant coping strategies (i.e., behaviors), negative mood states (i.e., emotion), and/or negative cognitive appraisals (cognition). Content codes were subsequently analyzed using multilevel models from a dynamical systems perspective to identify temporal change processes characteristic of suicide death. Results identified temporal sequences unique to suicide, notably social media posts about triggers followed by more posts about cognitions, posts about cognitions followed by more posts about triggers, and posts about behaviors followed by fewer posts about cognitions. Results suggest that certain sequences in social media content may predict cause of death and provide an estimate of when a social media user is likely to die by suicide.

Flow complexity in open systems: interlacing complexity index based on mutual information

Flow complexity is related to a number of phenomena in science and engineering and has been approached from the perspective of chaotic dynamical systems, ergodic processes or mixing of fluids, just to name a few. To the best of our knowledge, all existing methods to quantify flow complexity are only valid for infinite time evolution, for closed systems or for mixing of two substances. We introduce an index of flow complexity coined interlacing complexity index (ICI), valid for a single-phase flow in an open system with inlet and outlet regions, involving finite times. ICI is based on Shannon’s mutual information (MI), and inspired by an analogy between inlet–outlet open flow systems and communication systems in communication theory. The roles of transmitter, receiver and communication channel are played, respectively, by the inlet, the outlet and the flow transport between them. A perfectly laminar flow in a straight tube can be compared to an ideal communication channel where the transmitted and received messages are identical and hence the MI between input and output is maximal. For more complex flows, generated by more intricate conditions or geometries, the ability to discriminate the outlet position by knowing the inlet position is decreased, reducing the corresponding MI. The behaviour of the ICI has been tested with numerical experiments on diverse flows cases. The results indicate that the ICI provides a sensitive complexity measure with intuitive interpretation in a diversity of conditions and in agreement with other observations, such as Dean vortices and subjective visual assessments. As a crucial component of the ICI formulation, we also introduce the natural distribution of streamlines and the natural distribution of world-lines, with invariance properties with respect to the cross-section used to parameterize them, valid for any type of mass-preserving flow.

Behavioral Priming 2.0: Enter a Dynamical Systems Perspective.

On a daily basis, people are exposed to numerous stimuli, ranging from colors and smells to sounds and words, that could potentially activate different cognitive constructs and influence their actions. This type of influence on human behavior is referred to as priming. Roughly two decades ago, behavioral priming was hailed as one of the core forces that shape automatic behavior. However, failures to replicate some of the representative findings in this domain soon followed, which posed the following question: “How robust are behavioral priming effects, and to what extent are they actually important in shaping people's actions?” To shed a new light on this question, I revisit behavioral priming through the prism of a dynamical systems perspective (DSP). The DSP is a scientific paradigm that has been developed through a combined effort of many different academic disciplines, ranging from mathematics and physics to biology, economics, psychology, etc., and it deals with behavior of simple and complex systems over time. In the present paper, I use conceptual and methodological tools stemming from the DSP to propose circumstances under which behavioral priming effects are likely to occur. More precisely, I outline three possible types of the influence of priming on human behavior, to which I refer as emergence, readjustment, and attractor switch, and propose experimental designs to examine them. Finally, I discuss relevant implications for behavioral priming effects and their replications.

Adaptive culture and product/service innovation outcomes

Organizational culture has been used and defined extensively as a relatively stable, enduring set of values, beliefs, assumptions, and symbols shared in the organization. Based on this conception, researchers have studied the relationship between different types of cultures and innovation outcomes. In contrast to this static perspective, the dynamic systems perspective views culture as not necessarily determined by internalized and shared values. Rather, as cultures are constantly receiving environmental pressures that require continuous adaptation, they have an inherent attribute of change, which has been called adaptive culture. We focus on adaptive culture as an antecedent of product/service innovation outcomes, since innovations require a progressive upgrading of shared values, assumptions, and beliefs. Based on the thinking and acting schema, we propose two determinants of adaptive culture that help us to understand how culture can evolve to facilitate product/service innovation outcomes. We propose that structural flexibility and reflexive learning positively affect product/service innovation outcomes by creating an adaptive culture. A 190-company sample is used to analyze the theoretical model variables’ relationship to a culture that can change its values to improve product/service innovation outcomes. The results support the theoretical model, and lead to some implications for the management of culture.

Social Forces Sustaining the Israeli-Palestinian Tensions: A Dynamical Psychology Perspective

The Israeli-Palestinian conflict remains one of the most deeply entrenched in the world. While there is great knowledge about numerous individual factors contributing to this conflict’s persistence, much of the information is fragmented and segregated into different disciplines. This article seeks to integrate an array of literature using a dynamic systems perspective to examine how social – as opposed to political – forces contribute to ongoing tensions. The first part describes the dynamical systems perspective, focusing on how intractable conflicts emerge as a result of interlinking factors that anchor the social system in patterns that resist change. The second part explores the Israeli-Palestinian conflict in terms of specific social forces contributing to the conflict’s persistence. These include leadership issues, economic inequalities poverty, youth disenfranchisement, and population distribution. Then the contribution maps how the discussed social factors interrelate to reinforce the ongoing tensions and addresses how small-scale approaches may circumvent the volatile, entrenched patterns of hostility.

Relating emotional arousal to work vigour: A dynamic systems perspective

We examined the emotional basis of work engagement by focusing on the relationship between arousal and work vigour. Drawing on the DynAff model of Kuppens, Oravecz, and Tuerlinckx (2010), we looked at three elements underlying the temporal dynamics of arousal: (1) the level of baseline arousal (i.e., the attractor state around which arousal fluctuates), (2) the amount of variability in arousal around this baseline, and (3) the swiftness with which people return to their arousal baseline once they deviated from it. We conducted a five-day experience sampling study, in which 88 employees reported on their momentary core affect (i.e., their momentary level of valence and arousal), while vigour was measured at the end of the study. Results showed that higher levels of baseline arousal were related to increased levels of vigour. Furthermore, we found that baseline arousal interacted with arousal variability in the sense that only people with low levels of baseline arousal and low levels of arousal variability experienced lower levels of vigour. Together, our findings suggest that, if we want to advance our understanding of the emotional basis of work engagement, we need to look into the temporal dynamics underlying it.

Explaining motivation in language learning: a framework for evaluation and research

ABSTRACTResearching motivation in language learning is complex and multi-faceted. Various models of learner motivation have been proposed in the literature, but no one model supplies a complex and coherent framework for investigating a range of motivational characteristics. Building on previous models I propose such a methodological framework, based on a complex dynamic systems perspective, which re-conceptualises the investigation of motivation in second language acquisition in qualitative and mixed method approaches by offering one flexible tool for case study approaches. This new framework has been tried and tested in three locations in England and reported as case studies. The study aimed to address the following research questions: (1) In what ways does content and language integrated learning (CLIL) impact on learner motivation? (2) What are the main elements of CLIL that enhance motivation? Overall analysis of the results found that where expectations of success were high and where the teaching was effective, CLIL had a positive impact on motivation and progress. The framework is designed to be flexible enough to be used to investigate language learning in a range of national contexts. It is hoped that the proposed framework, reported here together with exemplification and commentary from the English study, will enable researchers in a wide range of language learning contexts to investigate learner motivation in a systematic and in-depth manner.

Phenotypic Plasticity and Cell Fate Decisions in Cancer: Insights from Dynamical Systems Theory.

Waddington’s epigenetic landscape, a famous metaphor in developmental biology, depicts how a stem cell progresses from an undifferentiated phenotype to a differentiated one. The concept of “landscape” in the context of dynamical systems theory represents a high-dimensional space, in which each cell phenotype is considered as an “attractor” that is determined by interactions between multiple molecular players, and is buffered against environmental fluctuations. In addition, biological noise is thought to play an important role during these cell-fate decisions and in fact controls transitions between different phenotypes. Here, we discuss the phenotypic transitions in cancer from a dynamical systems perspective and invoke the concept of “cancer attractors”—hidden stable states of the underlying regulatory network that are not occupied by normal cells. Phenotypic transitions in cancer occur at varying levels depending on the context. Using epithelial-to-mesenchymal transition (EMT), cancer stem-like properties, metabolic reprogramming and the emergence of therapy resistance as examples, we illustrate how phenotypic plasticity in cancer cells enables them to acquire hybrid phenotypes (such as hybrid epithelial/mesenchymal and hybrid metabolic phenotypes) that tend to be more aggressive and notoriously resilient to therapies such as chemotherapy and androgen-deprivation therapy. Furthermore, we highlight multiple factors that may give rise to phenotypic plasticity in cancer cells, such as (a) multi-stability or oscillatory behaviors governed by underlying regulatory networks involved in cell-fate decisions in cancer cells, and (b) network rewiring due to conformational dynamics of intrinsically disordered proteins (IDPs) that are highly enriched in cancer cells. We conclude by discussing why a therapeutic approach that promotes “recanalization”, i.e., the exit from “cancer attractors” and re-entry into “normal attractors”, is more likely to succeed rather than a conventional approach that targets individual molecules/pathways.

Motivational dynamics in English classes at a Brazilian public school

ABSTRACT This paper seeks to discuss the interrelations between the motivation of an English teacher at a public federal high school in Brazil and that of her students from a dynamic systems perspective. Three teachers’ oral narratives and 14 students’ logbook entries were used for the data analysis. The teacher’s descriptions and comments on her pedagogical practice were compared to the students’ impressions and motivational levels in an attempt to understand this English classroom-system motivational dynamics and self-organization processes, focusing on how this teacher’s and her students’ motivation co-adapt and soft-assemble, influencing and being influenced by the context. The results seem to confirm not only that student motivation and teacher motivation are deeply interrelated, but also that they interact organically and continually, revealing that the context is contingent.

A random dynamical systems perspective on stochastic resonance

We study stochastic resonance in an over-damped approximation of the stochastic Duffing oscillator from a random dynamical systems point of view. We analyse this problem in the general framework of random dynamical systems with a nonautonomous forcing. We prove the existence of a unique global attracting random periodic orbit and a stationary periodic measure. We use the stationary periodic measure to define an indicator for the stochastic resonance.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

Extensional channel flow revisited: a dynamical systems perspective

Extensional self-similar flows in a channel are explored numerically for arbitrary stretching-shrinking rates of the confining parallel walls. The present analysis embraces time integrations, and continuations of steady and periodic solutions unfolded in the parameter space. Previous studies focused on the analysis of branches of steady solutions for particular stretching-shrinking rates, although recent studies focused also on the dynamical aspects of the problems. We have adopted a dynamical systems perspective, analysing the instabilities and bifurcations the base state undergoes when increasing the Reynolds number. It has been found that the base state becomes unstable for small Reynolds numbers, and a transitional region including complex dynamics takes place at intermediate Reynolds numbers, depending on the wall acceleration values. The base flow instabilities are constitutive parts of different codimension-two bifurcations that control the dynamics in parameter space. For large Reynolds numbers, the restriction to self-similarity results in simple flows with no realistic behaviour, but the flows obtained in the transition region can be a valuable tool for the understanding of the dynamics of realistic Navier-Stokes solutions.

Exploring the Dynamic Long‐Term Interaction Between Cognitive and Psychosocial Resources in Adult Second Language Development at Varying Proficiency

This study draws on conceptual and methodological insights afforded within a dynamic systems perspective to explore shifting interrelationships between cognitive capacity and motivational resources in instructed adult second language (L2) learners of Spanish at increasing proficiency. Relationships that emerged showed both stability and fluctuation over a semester of instruction and varied by learners' stage of development. Findings support previous calls to improve upon mainstream approaches to conceptualizing and investigating learner individual differences (IDs) by adopting a holistic, non modular view of the L2 developmental system in general and learner IDs in particular (e.g., Dörnyei, , ; Dörnyei & Ryan, ; Larsen–Freeman, , , ). To further align with DST principles (see Hiver & Al‐Hoorie, ), future studies should endeavor to carry out longitudinal case studies with multiple data points in order to reveal intra‐individual complexity in the group‐level patterns seen here.

A dynamical system perspective on mathematical programming

ABSTRACTWe explore how to build a vector field from the various functions involved in a given mathematical programme, and show that equilibria of the corresponding dynamical system are precisely the solutions of the underlying optimality conditions for the original optimization problem. The general situation in which explicit inequality constraints are present is especially interesting as the vector field has to be discontinuous, and so one is led to consider discontinuous dynamical systems and their equilibria.

Heads Up!

Possibilities for behavior (i.e., affordances) can be perceived with units spanning anatomical components and external objects. For example, affordances for standing on an inclined surface can be perceived with an object held in the hand or attached to the head. We investigated whether these two person-plus-object perceptual systems exhibit the same pattern of nonlinear phase transitions in perception of this affordance. Blindfolded participants explored an inclined surface with a rod held in the hand or attached to the head and reported whether they could stand on that surface. Inclinations were presented in ascending or descending sequences. In both conditions, responses exhibited negative hysteresis - perceptual boundaries occurred at steeper angles for descending than for ascending sequences. The generalization of this pattern across components that differ in physiology, sensitivity, and experience is consistent with both the soft assembly of perceptual devices and with a dynamical systems perspective on perception of affordances.