Nonlinear Dynamics Psychology and Life Sciences最新文献

The DARPA OP TEMPO program seeks to accelerate warfighter readiness by supplying instructors with objective, automatic assessments of team performance during simulation training. To that end, we created the Bio-behavioral Team Dynamics Measurement System (BioTDMS), a multimodal sensing and analytics pipeline that discovers bio-behavioral 'signatures' emanating from within the human body and through team-member interactions that predict team performance. BioTDMS employs a layered symbolic dynamics model that converts time-aligned neural, cardio-respiratory, eye tracking, and verbal data, collected using a multimodal sensor suite. Moving-window entropy and mutual information computed across the symbolic sensor space yield real-time metrics that quantify team adaptability following perturbation (e.g., 'training injects') and distribution of team members' influence across biological and behavioral subsystems. These features feed a multitask, multi-kernel learning engine that refines performance prediction while preserving explainability through team construct mapping and a command-line user interface. We present preliminary results from field testing a full physical and computational implementation of BioTDMS during Fire Support Team (FiST) training exercises at the U.S. Marine Corps Air-Ground Combat Center, Twentynine Palms, CA. An onsite team instrumented five-person FiST crews with multimodal sensor suites. Sensor data were processed by BioTDMS for real-time and post hoc analytics. BioTDMS currently accounts for 90â¯% of variance in a subjective team perform-ance assessment made by instructors, with improvements expected upon further refinements of BioTDMS modeling components. These findings demonstrate BioTDMS's potential as an operational tool for automatic, objective team assessments. Future assessments within air combat teams, including configura-tions with human-autonomy teaming, will evaluate the generalizability of BioTDMS.

Effective motor coordination is essential for adaptive athletic performance, including musculoskeletal injury prevention, particularly in high impact activities. Understanding how the lower extremities adapt to added constraints, such as increased load, can provide valuable insights into the resilience of movement patterns. This study examined the influence of added load on intralimb coordination during a drop-vertical jump (DVJ). Twenty-six participants (14 female, age = 23.10 +/- 3.97 years, 76.81 +/- 18.73kg) performed 5 body weight DVJs and 5 with an additional 25% body weight using a weighted vest. 3D joint kinematics were recorded using OpenCap markerless motion capture (OpenCap, Menlo Park, CA). Linear measures were calculated for the knee and hip, while nonlinear cross recurrence quantification analysis indexed intralimb coordination between the knee and hip joints. Alpha level was set a priori at α=.05. Paired-sample t-tests revealed smaller peak knee flexion (p=.013) and decreased total range of motion in both the hip and knee (p=.014 and .013, respectively) in the +25% body weight condition. Additionally, recurrence rate (p=.036), determinism (p=.046), adjusted mean line (p=.023), and adjusted trapping time (p=.011) were all lower in the +25% body weight condition. These results indicate that weight-based constraints lead to stiffer landing mechanics and noisier, less tightly coupled intralimb coordination. These findings highlight the need to consider both the landing mechanics and coordination dynamics when considering the implementation of movement assessments for athletic performance and injury prevention under increased load conditions.

The COVID-19 pandemic exhibited some interesting temporal dynamics that were not accounted for by the traditional susceptible-infectious-recovered (SIR) family of epidemic time series models. The recorded number of positive tests increased and declined in waves from March 2020 to October 2021. Additional variability appeared around the peak of the waves. The present study examined the time series of positive medical tests reported by the public health system for one U.S. State. The wave patterns were consistent with the probability density function associated with the swallowtail catastrophe model. The excess variability was hypothesized to be indicative of workload stress in the health care system. The analysis of residuals from the swallowtail model was consistent with the probability density function of the cusp catastrophe model, which is known as a viable model for changes in system performance under conditions of changing workload. The two functions together accounted for 96% of the variance in daily positive test reports. A chaotic model was also tested as an alternative to the cusp. Although it contained some informative dynamics, it was not as accurate as the cusp interpretation. Implications for modeling and forecasting future epidemics are discussed.

This study investigates the relationship between two phenomena that can emerge simultaneously in group interaction: autonomic synchrony and the emergence of leadership roles among team members. It was previously shown that the probability distributions of the two processes are both phase shifts characterized by the swallowtail catastrophe distribution. The objective of the present study was to examine the role of team autonomic synchrony as one of the three control parameters in the leadership emergence model. Research participants were 136 undergraduates who were organized into teams of three to five members playing the computer-game Counter-Strike while wearing GSR sensors. After approximately two hours of interaction, team members rated each other on leadership behaviors. Autonomic synchrony was analyzed as a driver-empath process that produces a group-level coefficient of synchrony (SE) from dyadic interactions from all possible dyadic interactions. The model was built in three stages: (a) replicate a model obtained from a similar team effort involving dynamic decisions, (b) test new variables as specific control parameters, and (c) test synchrony metrics for their best fit as one of the three control parameters. Results showed that both synchrony metrics were best understood as bifurcation variables that brought individuals who were already in the zone of potential leaders into primary or secondary roles. Prior gaming experience and SE Variability each played the role of a bias variable that distinguished between primary and secondary leaders. SE Variability also pre-empted team performance as a control variable.

This paper investigates the fate of original information after it is synthesized into knowledge, focusing on whether it becomes entangled within the integrated knowledge or if it is retained as isolated units in long-term memory. We explore two possibilities: either synthesized knowledge removes original information from memory, or both integrated and insulated information are stored once the information becomes part of knowledge. To address these questions, we simulate problem-solving processes over time. The simulation reveals how, once information is integrated into knowledge, it is removed as an insulated piece of information in the long-run memory. The process also shows a dynamic interplay between the creation and fragmentation of knowledge induced by synthetic and analytical skills. Furthermore, we examine whether the analytical-synthetic (A-S) process, as an autonomous system, tends to diverge or converge toward a steady state. We identify a stable stationary point in the process of knowledge creation and fragmentation. We also discuss how novelty can emerge exogenously (through errors) or endogenously and how it oscillates within the (A-S) process when heterogeneous information and knowledge are considered. Finally, we identify a chaotic transition in the way knowledge and theories compensate for reduced information.

We propose the novel hypothesis that Seasonal Affective Disorder (SAD), and seasonal mood variation more generally, may be influenced in part by how seasonal changes influence people's exposure to fractals in their environments - for example, due to changes in vegetation and cloud cover. This general hypothesis implies that seasonal mood variation may occur not only in high latitudes but also in tropical areas where wet-dry seasons alter the fractal character of the environment. Based on our general hypothesis, we develop a series of specific hypotheses about where seasonal mood variation may be most prevalent. We also discuss potential reasons for variation in mood across individuals and propose testable treatment options for those experiencing SAD.

This study evaluated autonomic synchrony and variability in synchrony as control variables in cusp catastrophe models of workload and fatigue for teams making dynamics decisions. In this experiment, 136 undergraduates were organized into 32 groups of three, four, and five members playing an online computer game while wearing electrodermal sensors. They also completed cognitive measures of elasticity-rigidity and situation awareness during the games. Synchrony was calculated using the SE coefficient from the driver-empath model. Analyses were constructed to determine whether SE or SE variability added value to the cusp catastrophe models for cognitive workload and fatigue that were determined previously in Guastello and McGuigan (2024). Results indicated that SE made a strong impact on changes in performance as a bifurcation variable in both the workload and fatigue models. The positive and negative impact on performance shown by SE variability suggested that team members were grappling with the best ways to coordinate with each other or that teams found an advantage to turning synchrony on and off. SE variability made a strong impact as a bifurcation variable in the workload load model, and a strong impact as a compensatory ability (asymmetry parameter) in the fatigue model. Practical implications are that synchrony could be functional or dysfunctional, depending on situational demands that could be momentary. The study opened new questions regarding the qualitative relationship between elasticity-rigidity variables and synchrony and the possible roles of strong vs. weak ties in a closed network of this type.

The present research develops a formal mathematical model to measure individual motivation at work. Its mathematical specifications correspond to a formal translation of Conservation of Resources (COR) theory core assumptions. It explores how such COR constructs as resource caravan and resource passageway determine patterns of motivational processes. The model is applied to a sample of working professionals (n = 8) from different occupations. Data is obtained from a 5-item Likert-scale questionnaire based on the COR-Evaluation (COR-E) instrument developed by Hobfoll et al. (1992). Results are presented in the form of eight tables that correspond to eight different resource caravans. They unveil how individual motivational processes vary by the extent to which resources interact with an underlying drive for preservation. The role of context is also confirmed as a resource passageway. With regard to methodology, this research emphasizes how measurement based on mathematical modeling can be an alternative to standard data-analytic statistics. At a theory level, it enriches both COR-based literature and theory of workplace motivation. Practically, it provides an analytical instrument that details information on those processes that shape individual motivational profiles in organizations.

In this paper we introduce a mathematical model of psychotherapy where the proposed continuous dynamical system describes the relationship between the client and the therapist based on dyadic interactions modeling. The model also incorporates the influence representing the external environment of the client's state. It was assumed that given that influence is negative. In order to reflect possible instability of the client's state, we describe the client who can demonstrate other mental comorbidities. Additionally, this assumption depicts the fact that his/her preferable state can be not unique. We analyse basic properties of the model and use it to study various scenarios of final results of psychotherapy.

In this article we model two-strain dynamics of dengue transmission by both mosquitoes and humans with vertical transmission to larvae in the mosquito population. We include secondary infections, causing a severe form of disease. Mathematical analysis of proposed model is conducted - we study existence and local stability of equilibria of the system. While analysing the model, unusual properties emerged which lead us to implement its simplifications and obtain different results. Theoretical outcomes are accomplished with numerical simulations. They suggest that vertical transmission has a negligible impact on the dengue spread.