Nonlinear Dynamics Psychology and Life Sciences最新文献

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.

This year's cover artist, Clinton Marstall, creates fractal imagery by photographing natural patterns. He then uses a pen to highlight the patterns and projects them on to a canvas for painting. Using this precision technique, he integrates a number of biomorphic images into a unique amalgum of multi-scaled complexity.

Mathematical modeling and computer simulations are important means to understand the mechanisms of psychotherapy. The challenge is to design models which not only predict outcome, but simulate the nonlinear trajectories of change. Another challenge is to validate them with empirical data. We proposed a model on change dynamics which integrates five variables (order parameters) (therapeutic progress or success, motivation for change, problem severity, emotions, and insight) and four control parameters (capacity to enter a trustful cooperation and working alliance, cognitive competencies and mindfulness, hopefulness, behavioral resources). The control parameters modulate the nonlinear functions interrelating the variables. The evolution dynamics of the system is determined by a set of nine nonlinear difference equations, one for each variable and parameter. Here we outline how the model can be tested and validated by empirical time series data of the variables, by time series of the therapeutic alliance, and by assessing the input onto the system as it is perceived by the client. The parameters are measured by questionnaires at the beginning and at the end of the treatment. A key element of the validation algorithm is the adjustment of the parameter values as assessed by the questionnaires to model-specific parameter values by which the dynamics can be reproduced (calibration). The validation steps are illustrated by the data of a client who used an internet-based tool for high-frequency therapy monitoring (daily self-ratings). Especially after applying the input vector (interventions as experienced by the client) the similarity between the empirical and the model dynamics becomes evident. The averaged correlation between the empirical and the simulated dynamics across all variables is .41, after applying a short averaging mean window and eliminating an initial transient period, it is .62, varying between .47 and .81, depending on the variable. The discussion opens perspectives on the combination of mathematical modeling with real-time monitoring in order to realize data-driven simulations for short-term predictions and to estimate the effects of interventions before real interventions are applied.

In this article we study the application of nonlinear indices (sometimes called complexity indices) to univariate time series data arising from studies of schizophrenia and bipolar disorder. Specifically, we consider time series arising from EEG studies, ECG studies, and self-report mood data. As part of our analysis, we empirically examine the claim in the literature that complexity tends to be higher in the EEG of schizophrenia patients than controls and that this tendency is dampened or even inverted by medication, increasing age, and reduced symptomatology. Our conclusion is that this claim is only partially supported and propose that symptomatology, specifically the presence or absence of schizophrenia 'deficit syndrome,' may be the most important factor. Results are more consistent in ECG studies in which reduced heart rate complexity is observed in both schizophrenia and bipolar disorder. The applications of nonlinear indices to the effects of antipsychotic medication and the discrimination of mood states are also examined. It is concluded that the monitoring of nonlinear indices may be useful in predicting response to medication and predicting onset of specific mood states.

Since the mid 1980's, mainstream social psychology investigations of self-complexity and psychopathology have produced contradictory results. These results are likely the result of a lack of theoretical and methodological grounding in complexity theory. The current study proposes that the self has an interconnected fractal structure, and that this structure may be reflected within inverse-power law (IPL) distributions of response times to self-related questions. MMPI-2 item response sets (N = 300) were selected from a larger pool of 1,881 forensic administrations. Self-complexity was operationalized as the inverse of the shape parameter (?) of the frequency distribution of reaction times to MMPI-2 items (n = 567) for each participant. It was predicted that: (a) these distributions would generally have strong fits with IPL distributions; and (b) that ? would tend to be correlated with pathology among the MMPI-2 scale scores. The results confirmed that the response-time distributions tended to fit IPLs (mean R2 = .94; range: .64 to .99). Furthermore, 18 of 45 correlations between ? and MMPI-2 scale scores associated with pathology were statistically significant, suggesting that rigidity in fractal self-structure is associated with broadband psychopathology. A follow up principal components analysis of the 45 individual scale scores across the participants confirmed this conclusion, producing three latent components, each of which was significantly correlated with ?, and each of which had a broad variety of scales with factor loadings > |.5|. These results may provide a first step toward a practical complexity-science approach to measuring the structural resilience of the self, and viewing the self as a complex self-organizing system.