Nonlinear Dynamics Psychology and Life Sciences最新文献

This article investigates the chaotic relationship among inflation rate, unemployment rate and oil prices over the period of January, 1974-October, 2018 in the USA. This study complements the previous studies on this subject. However, it differs from the existing literature in examination of inflation-unemployment trade-off by a neural network model in which oil prices are considered as an exogenous variable, and in analyzing the chaotic causality relation among the variables by Hristu-Varsakelis and Kyrtsou and Bai nonlinear causality tests. The results first suggested by a class of neural network model, which was the multi-layer perceptron (MLP), pointed out an important relation among the analyzed variables. Accordingly, oil price changes have substantial effect on unemployment and inflation. Following, the empirical findings of Hristu-Varsakelis and Kyrtsou and Bai nonlinear causality tests show that there is a unidirectional chaotic relation from oil price to inflation, from oil price to unemployment and from inflation to unemployment.

The problem being investigated is the spontaneous rise of the herd instinct in an artificial population, where the population members have no explicit stimuli for such instinct implanted in their original behavior patterns. The simulations are focused on the creation of the herd instinct in the population that originally does not reveal the gregarious behavior. In this agent-based model, the members of the population (agents) move over a limited region, look for renewable food, reproduce, escape a threat and die. The evolution of the herd instinct is simulated. No explicit instinct is imposed on the agents. It is pointed out that both the search for food and fear, are factors that cause the rise of the herd instinct. The evolution of the amount of the instinct is slow and needs long model time intervals, running over several hundred generations of the agents. The herd instinct is inherited from parent agents during the reproduction, with certain random deviation. This makes the total amount of the instinct evolve and reach an optimal level.

Human dyads and larger teams tend to acquire synchronized movements and autonomic arousal levels while working together or simply socializing. The synchronization of arousal patterns is of theoretical interest for group dynamics because they may add predictive value to the dynamics of group cohesion and team performance. This study examined the four-way relationship among experimental conditions: team size, task difficulty, time pressure (between-subjects) and subsequent experimental sessions (within-subjects). Previously, we have shown these conditions affect subjective ratings of workload that come from individual and group-level sources, synchronization of arousal, and team performance. In an experiment involving an emergency response (ER) simulation, 360 undergraduates, who were wearing electrodermal sensors, were organized into 44 teams of various sizes. Workload was experimentally varied by team size (three, four, seven or eight members), number of opponents (one or two), and time pressure; the latter was introduced sooner or later across two experimental sessions. Results showed that the experimental conditions affected synchronization levels, either at the beginning of a session or in the middle; synchronization and experimental conditions were not directly related to team performance. Subjective group workload ratings of the coordination demand of the task correlated with synchronization at the beginning of a session while team satisfaction was correlated with greater synchrony at the end of a session. The competitive nature of the ER task, as compared to strictly cooperative tasks, could be responsible for the complexity of these empirical relationships.

Under the premise that language learning is bidirectional in nature, this study aimed to investigate syntactic coordination within teacher-student interactions by using cross-recurrence quantification analysis (CRQA). Seven teachers' and a group of their students' interactions were repeatedly measured in the course of an intervention in early science education. Results showed changes in the proportion of recurrent points; in case of simple sentences teachers and students became less coordinated over time, whereas in case of complex sentences teachers and students showed increasing coordination. Results also revealed less rigid (more flexible) syntactic coordination, although there were no changes in the relative contribution of teacher and students to this. In the light of the intervention under investigation this is an important result. This means that teachers and students learn to use more complex language and coordinate their language complexity better in order to co-construct science discourse. The application of CRQA provides new insights and contributes to better understanding of the dynamics of syntactic coordination.

In the present paper, the Rosenzweig-MacArthur predator-prey model (RM), which is a bitrophic food chain model, is considered. We develop the model by adding two assumptions. First, we assume that both species are of economic interest, that is can be harvested. Second, we assume that each specie has its own time scale which range from fast for the prey to slow for the predator. We consider that both the death rate and the catch of the predator are very small which leads to a fast-slow dynamical system. That is, the RM model is transformed into a singular perturbed system with a perturbation parameter E in the set [0,1]. The existence and stability of equilibria are discussed for E > 0. The model experiences both transcritical and Hopf bifurcations for E>0. The singular perturbation model at E = 0 is discussed by separating the system into two subsystems; fast and slow and studying them simultaneously. When 0

Previous research has found that spontaneous synchronization of bodily movements emerges when people interact. This dynamic interactional synchrony occurs in all kinds of everyday movements and has been demonstrated empirically in a variety of social contexts. The objective of this study is to advance our understanding of the dynamical processes that enable the spontaneous and fluid coordination of movements in more naturalistic social interactions. We measured the degree of interactional synchrony of 44 dyads who enacted a series of knock-knock jokes together and we manipulated the perceptual information available (using auditory occlusion) and the individuals' dynamical motor 'signatures' by weighting their limbs. Our analyses using relative phase and fractal/multifractal measures support the conclusion that both local and global dynamical synchronization processes sustain the interactional fluidity seen in conversational exchanges and provide an embodied foundation for how humans connect and cooperate socially.

Psychoanalysts and therapists have noticed that the increase of the reconciliation time, i.e., the period of dissatisfaction that two lovers need to return to their positive equilibrium after a dispute, is often a warning sign of an impending consistent drop of quality of the relationship, possibly followed by a breakup (e.g., a divorce). Here this rule is investigated and shown to be the logical consequence of the attitude of individuals (here called secure) who increase their reaction when their partners get more involved. The analysis is carried out with a well-known and repeatedly validated mathematical model composed of two nonlinear differential equations and the rule follows from the discovery that the model has catastrophic bifurcations with respect to the psychophysical traits of the partners. Thus, for example, negative trends in the appeal of the partners or in the reactiveness to it slowly but inevitably push couples toward a tipping point, from which a critical transition can originate. Since the rule is here justified only for couples composed of secure individuals, finding out if it holds also for other couples remains an interesting open problem.

Most relevant theories of prosocial behavior aim at exploring and understanding helping motivations from an evolutionary perspective. This article summarizes findings from research on prosocial behavior from both a socio-economic and psychological perspective. Building on literature exploring the basic processes and determinant variables of helping, we propose a stochastic and dynamic model to simulate prosocial behaviors over time and recreate evolutionary processes of helping behaviors. Such a mathematical model formalizes a procedure for dynamic simulations, including agent-based modeling, which implies non-linear dynamics of prosocial processes underlying helping motivations. Practical implications for organizations and societies are addressed.

The purpose of this study was to develop a mathematical model of mutual partner violence and assess impact of her controllable behaviors on reducing violence. An agent-based model was created of couples with history of violence based upon results of two multiple time series studies of partner violence. To explore factors that may alter model results, eight continuous variable parameters were created based upon significant (p=.05) but discrepant (opposite directions) results from previous studies. To assess the potential impact that random stress and her behavior (arguments, forgiveness, alcohol use, violence) could have on violence and stalking, the impact of variable parameter settings of these factors were also assessed. The model identified 18 unique patterns were observed, grouped into five general categories. Added random stress contributed to his violence in only two patterns. Although avoiding participation in arguments had no effect, her forgiveness and elimination of alcohol use often reduced her violence only. However, consistent violence or nonviolence on her part sometimes affected his violence and stalking. In conclusion, while increasing forgiveness and reducing alcohol intake could reduce her violence, they generally had little effect on his. However, if she eliminated her violence, it could eliminate his violence and stalking in some situations.

This introduction to a special issue of Nonlinear Dynamics, Psychology and Life Sciences discusses the contributing articles within the issue from a variety of perspectives. This analysis examines each article's contribution to understanding the self, and to exploring the application of innovative nonlinear methods to clinical questions. Moving beyond the special issue, the analysis examines the role of nonlinear science in clinical psychology from the perspective of Aristotle's four types of cause: material, efficient, formal and teleological. It is suggested that nonlinear science is particularly well-suited to empirical science aimed at understanding formal (i.e., systemic), and teleological (dynamical) causes. The strength of nonlinear dynamical systems methods in addressing formal and teleological cause could help bridge the gaps in understanding clinical phenomena using the medical model, which focuses primarily on material and efficient causes. Finally, a list of the top ten nonlinear dynamical systems concepts is presented with the goal of direct applications that may be useful for clinicians.