Nonlinear Dynamics Psychology and Life Sciences最新文献

Synchronization is a special case of self-organization in which one can observe close mimicry in behavior of the system components. Synchrony in body movements, autonomic arousal, and EEG activity among human individuals has attracted considerable attention for their possible roles in social interaction. This article is specifically concerned with autonomic synchrony and finding the best model for the dyadic relationships, with regard to both theoretical and empirical accuracy, that could be extrapolated to synchrony levels for groups and teams of three or more people. The four models that are compared in this study have different theoretical origins: the two-variable linear regression function, a three-parameter nonlinear regression function, the logistic map function stated in polynomial form, and the logistic map function stated as an exponential regression structure. The data for this study were electrodermal responses collected from a team of four people engaged in an emergency response simulation that produced 12 dyadic time series. Results shows strong levels of fit between the data and all four models, although there were significant differences among them. Further research directions point toward finding conditions that favor one model over another and exploring other possible nonlinear structures.

The analysis of handwriting has been used in several contexts. For example, handwriting has shown to be of value in the study of motor symptoms in neurological and mental disorders. In the present work, the geometric analysis of handwriting patterns is proposed as a tool to evaluate motor symptoms in psychotic disorders. Specifically, we have employed the lacunarity, a measure of the heterogeneity of a spatial structure. Forty-two patients with a psychotic disorder and 35 matched healthy controls participated in the study. Participants were asked to copy some patterns with a pen on a white paper. The results showed that lacunarity was significantly higher in handwritten patterns from patients than from controls. In addition, we found higher values of lacunarity in handwritten patterns from patients with severe motor symptoms in comparison with patients with mild or absent motor symptoms. Lacunarity of handwritten patterns was significantly correlated with clinical scores of rigidity. In conclusion we argue that the heterogeneity of handwritten patterns could be used as a simple and objective measure of motor symptoms.

Heart rate variability (HRV) is a noninvasive marker of cardiac autonomic function that has been extensively studied in a variety of populations. However, HRV analyses require stationarity-thus, limiting the conditions in which these data can be analyzed in physiologic and health research (e.g. post-exercise). To provide evidence and clarity on how non-stationarity affects popular indices of variability and complexity. Simulations within physiologic (restricted to values similar to exercise and recovery RR-intervals) and non-physiologic parameters, with homoscedastic and heteroscedastic variances, across four sample lengths (200, 400, 800, and 2000), and four trends (stationary, positive-linear, quadratic, and cubic) were detrended using 1-3 order polynomials and sequential differencing. Measures of variability [standard deviation of normal intervals (SDNN) and root mean square of successive differences (rMSSD)] as well as complexity [sample entropy (SampEn)] were calculated on each of the raw and detrended time-series. Differential effects of trend, length, and fit were observed between physiologic and non-physiologic parameters. rMSSD was robust against trends within physiologic parameters while both SDNN and SampEn were positively and negatively biased by trend, respectively. Within non-physiologic parameters, the SDNN, rMSSD, and SampEn of the raw time-series were all biased, highlighting the effect of the scale between these two sets of parameters. However, indices of variability and complexity on the original (trended) times-series were furthest from those of the stationary time-series, with indices coming closer to the known values as fit become more optimal. Detrending with polynomial functions provide reliable and accurate methods of assessing the variability and complexity of non-stationary time-series-such as those immediately following exercise.

This paper aimed to (a) validate a novel technique that quantifies the length of the trajectories the cardiac system follows within a two-dimensional state-space, and (b) test its usefulness to better understand how cognitive emotion regulation (CER) style is associated with cardiac output. A positive CER style was assessed in a sample of healthy adolescents (n = 57), and mean and total distances, in addition to heart rate variability (HRV) measures and cardiac entropy (SampEn), were calculated during a conflict discussion with the adolescents' mothers. Associations between distances and HRV measures in time and frequency-domains and SampEn were examined to better understand the physiological meaning of distances; further, whether a positive CER style would predict distances, HRV, and SampEn. Correlation analysis revealed that associations of distances with time-domain HRV measures were stronger than associations with frequency-domain HRV measures, while correlations between distances and SampEn were moderate. Hierarchical multiple regression analysis revealed that a positive CER style predicted distances and SampEn, but not HRV measures. Distances are clearly time-domain measures of HRV, but only partly capture the complexity of the heart signal. The results highlight the importance of assessing heart rate dynamics beyond HRV in the study of CER.

This article presents the geometrical-fractal text-tree model of speech and writing, the development of which is part of a project with the long-term goal to answer the question whether Artificial Intelligence and the corresponding human intelligence are principally different or not. Text-tree models consist of word-shrubs 'glued' together by syntax. Word-shrubs are designed by means of two principles, one is the dictionary or semantic principle that we can explain all verbal meanings by the meanings of other words. The other is the initiator-generator procedure, used to develop geometrical fractals. The structure of the word-shrub grows from the root-word when the meaning of the root-word, the generator, is connected as a branch to the root-word which is first initiator. Then all generator words are redefined as new initiators and connected to their meaning, the second generators. But the words or these are redefined as new initiators, each then being connected to its generator-meaning. This is repeated ad infinitum. Each new layer of generators represents a branching level. Consistency of verbal meaning is achieved by fixing the number of branching levels of the word-shrub. Wobbling consistency occurs when the talking or writing person shifts between levels of branching. We develop the M-method, important for most of the results, because it allows differences in verbal meaning to be estimated numerically. An interesting property of the text-tree model is revealed by showing that there must exist a cloud of unexperienced meaning variants of human texts. Most interesting, perhaps, is the demonstration of what we call the lemma of incompleteness which states that humans cannot prove beyond doubt, that they understand correctly what they say and write. This lemma seems to be a distant barrier for the expansion of human understanding and of relevance for understanding human versus artificial intelligence.

Persuasive technology can support users of self-paced eLearning courses during critical moments of low motivation. Agent-based models (ABMs) - a relatively unfamiliar phenomenon within the persuasive technology and eLearning domains- offers a potentially relevant methodology to understand when the support should be delivered. Using ABMs, the dynamics of motivational user states can be simulated. Subsequently, emerging user patterns can be traced that can potentially provide insight in the ebb and flow of motivation. For the purpose of this study, we designed an exploratory ABM on motivation based on the mental energy notion of which the foundations can be found both within the literature of motivational psychology and agent-based modeling. During the simulations we succeeded in generating moments of critically low user motivation. In addition, we were able to simulate the positive impact of external user support at those critical moments. These results suggest that it is plausible to put further energy in developing ABM models with the ultimate goal of feeding persuasive technology with the ability to deliver just-in-time user support during eLearning.

Synchronization of autonomic arousal levels within dyads and larger teams has been associated with several types of social-behavioral outcome. One previous study reported greater physiological influence (brain activity in one area of the parietal lobe associated with verbal activity) of leaders on followers than of followers on leaders; influence was measured pairwise within triadic problem solving groups. The present study explored synchronized autonomic arousal with leadership outcomes in two experiments with group sizes of three to eight members. Drivers, who had the greatest physiological impact on other team members were consistently less like the leader of the group. Empaths, who were the most receptive to autonomic signals from others, were not consistently associated with leadership roles, although they did show sensitivity to team dynamics in their ratings of cognitive and social sources of workload. The tentative conclusion, subject to future research, is that successful leadership requires a balance between the driver and empath orientations.

This study explored the chaotic properties of human emotions as expressed in social media and its implications for attainable forecasting horizons. Three human emotional states extracted from Twitter were analyzed using the nonlinear dynamics approach. The greatest positive Lyapunov exponent (LE) and 0-1 test methods were applied to a time series set consisting of over 25,000 data points reflecting the hourly recorded data of over 1.3 million tweets. The results suggest that the examined emotional time series data represent a nonlinear dynamical system with deterministic chaos properties. Therefore, by utilizing traditional linear methods of social media data analysis, one may not be able to fully understand and forecast critical transition trends over time or beyond a limited duration. It was concluded that the nonlinear dynamics approach is useful to determine a feasible forecasting horizon and to assess the prediction accuracy of social media data in general.

In the current study, we applied the dynamical systems approach to obtain novel insights into resilience losses. Dyads (n = 42) performed a lateral rhythmical pointing (Fitts) task. To induce resilience losses and transitions in performance, dyads were exposed to ascending and descending scoring scenarios. To assess changes in the complexity of the dyadic pointing performance, reflecting their resilience, we performed cross-recurrence quantification analyses. Then, we tested for temporal patterns indicating resilience losses. We applied lag 1 autocorrelations to assess critical slowing down and mean squared successive differences (MSSD) to assess critical fluctuations. Although we did not find evidence that scoring scenarios produce performance transitions across individuals, we did observe transitions in each condition. Contrary to the lag 1 autocorrelations, our results suggest that transitions in human performance are signaled by increases in the MSSD. Specifically, both positive and negative performance transitions were accompanied with increased fluctuations in performance. Furthermore, negative performance transitions were accompanied with increased fluctuations of complexity, signaling resilience losses. On the other hand, complexity remained stable for positive performance transitions. Together, these results suggest that combining information of critical fluctuations in performance and complexity can predict both positive and negative transitions in dyadic team performance.

The adjacency matrix of a weighted directed graph contains information on both connectivity and the strength of that connection. When the special case of Markov chains are considered, the additional constraints permit the characterization of the eigenvalues of its transition matrix, and the change of the nature of those eigenvalues as the probabilities (weights) change. A change in the nature of the eigenvalues, bifurcations, signals a change in the dynamic approach to a limiting probability of a chain as well as other aspects that can be of interest in applications. In this paper, we first characterize eigenvalues of any weighted directed cycles and any 3-state Markov chain. Then we define and characterize a special case, zero trace chains, which is useful in an ecology application discussed.