Societies with fission-fusion dynamics as complex adaptive systems: the importance of scale.

In this article, we argue that social systems with fission-fusion (FF) dynamics are best characterized within a complex adaptive systems (CAS) framework. We discuss how different endogenous and exogenous factors drive scale-dependent network properties across temporal, spatial and social domains. Importantly, this view treats the dynamics themselves as objects of study, rather than variously defined notions of static 'social groups' that have hitherto dominated thinking in behavioural ecology. CAS approaches allow us to interrogate FF dynamics in taxa that do not conform to more traditional conceptualizations of sociality and encourage us to pose new types of questions regarding the sources of stability and change in social systems, distinguishing regular variations from those that would lead to system-level reorganization. This article is part of the theme issue 'Connected interactions: enriching food web research by spatial and social interactions'.

Single-Molecule Imaging of Integral Membrane Protein Dynamics and Function.

Integral membrane proteins (IMPs) play central roles in cellular physiology and represent the majority of known drug targets. Single-molecule fluorescence and fluorescence resonance energy transfer (FRET) methods have recently emerged as valuable tools for investigating structure-function relationships in IMPs. This review focuses on the practical foundations required for examining polytopic IMP function using single-molecule FRET (smFRET) and provides an overview of the technical and conceptual frameworks emerging from this area of investigation. In this context, we highlight the utility of smFRET methods to reveal transient conformational states critical to IMP function and the use of smFRET data to guide structural and drug mechanism-of-action investigations. We also identify frontiers where progress is likely to be paramount to advancing the field.

Early social-cognitive development as a dynamic developmental system-a lifeworld approach.

Based on developmental systems and dynamic systems theories, we propose the lifeworld approach-a conceptual framework for research and a hypothesis concerning early social-cognitive development. As a framework, the lifeworld approach recognizes the social embeddedness of development and shifts the focus away from individual developmental outcomes toward the reciprocal interplay of processes within and between individuals that co-constitutes early social-cognitive development. As a hypothesis, the lifeworld approach proposes that the changing developmental system-spanning the different individuals as their subsystems-strives toward attractor states through regulation at the behavioral level, which results in both the emergence and further differentiation of developmental attainments. The lifeworld approach-as a framework and a hypothesis, including key methodological approaches to test it-is exemplified by research on infants' self-awareness, prosocial behavior and social learning. Equipped with, first, a conceptual framework grounded in a modern view on development and, second, a growing suite of methodological approaches, developmental science can advance by analyzing the mutually influential relations between intra-individual and interactional processes in order to identify key mechanisms underlying early social-cognitive development.

Nonlinear EEG Analysis During Motor and Cognitive Tasks in Patients With Long COVID: A Dynamic Systems Approach.

Introduction. Nonlinear EEG provides information about dynamic properties of the brain. This study aimed to compare nonlinear EEG parameters estimated from patients with Long COVID in different cognitive and motor tasks. Materials and Methods. This 12-month prospective cohort study included 83 patients with Long COVID: 53 symptomatic and 30 asymptomatic. Brain electrical activity was evaluated by EEG in 4 situations: (1) at rest, (2) during the Trail Making Test Part A (TMT-A), (3) during the TMT Part B (TMT-B), and (4) during a coordination task: the Box and Blocks Test (BBT). Nonlinear EEG parameters were estimated in the time domain (activity and complexity). Assessments were made at 0 to 3, 3 to 6, and 6 to 12 months after inclusion. Results. There was a decrease in activity and complexity during the TMT-A and TMT-B, and an increase of these parameters during the BBT in both groups. There was an increase in activity at rest and during the TMT-A in the COVID-19 group at 0 to 3 months compared to the control, an increase in activity in the TMT-B in the COVID-19 group at 3 to 6 months compared to the control, and reduced activity and complexity at rest and during the TMT-A at 6 to 12 months compared to the control. Conclusion. The tasks followed a pattern of increased activity and complexity in cognitive tasks, which decreased during the coordination task. It was also observed that an increase in activity at rest and during cognitive tasks in the early stages, and reduced activity and complexity at rest and during cognitive tasks in the late phases of Long COVID.

Comparing the advantages and disadvantages of physics-based and neural network-based modelling for predicting cycling power.

Models of physical phenomena can be developed using two distinct approaches: using expert knowledge of the underlying physical principles or using experimental data to train a neural network. Here, our aim was to better understand the advantages and disadvantages of these two approaches. We chose to model cycling power because the physical principles are already well understood. Nine participants followed changes in cycling cadence transmitted through a metronome via earphones and we measured their cadence and power. We then developed and trained a physics-based model and a simple neural network model, where both models had cadence, derivative of cadence, and gear ratio as input, and power as output. We found no significant differences in the prediction performance between the models. Both models had good prediction accuracy despite using less input variables than traditional models and using more challenging prediction conditions by enforcing rapid speed changes during cycling. The advantages of the neural network model were that, for similar performance, it did not require an understanding of the underlying principles of cycling nor did it require measurements of fixed parameters such as system weight or wheel size. These same features also give the physics-based model the advantage of interpretability, which can be important when scientists want to better understand the process being modelled.

Edge-based relative entropy as a sensitive indicator of critical transitions in biological systems.

BACKGROUND: Disease progression in biosystems is not always a steady process but is occasionally abrupt. It is important but challenging to signal critical transitions in complex biosystems. METHODS: In this study, based on the theoretical framework of dynamic network biomarkers (DNBs), we propose a model-free method, edge-based relative entropy (ERE), to identify temporal key biomolecular associations/networks that may serve as DNBs and detect early-warning signals of the drastic state transition during disease progression in complex biological systems. Specifically, by combining gene‒gene interaction (edge) information with the relative entropy, the ERE method converts gene expression values into network entropy values, quantifying the dynamic change in a biomolecular network and indicating the qualitative shift in the system state. RESULTS: The proposed method was validated using simulated data and real biological datasets of complex diseases. The applications show that for certain diseases, the ERE method helps to reveal so-called "dark genes" that are non-differentially expressed but with high ERE values and of essential importance in both gene regulation and prognosis. CONCLUSIONS: The proposed method effectively identified the critical transition states of complex diseases at the network level. Our study not only identified the critical transition states of various cancers but also provided two types of new prognostic biomarkers, positive and negative edge biomarkers, for further practical application. The method in this study therefore has great potential in personalized disease diagnosis.

Perseveration on suicidal thoughts and images in daily life: An examination of the cognitive model of suicide through a dynamic systems lens.

According to the cognitive model of suicide, interactions between hopelessness and attentional biases toward suicidal information create a narrowed attentional focus on suicide as a viable solution, particularly in the presence of life stress, leading to increased suicide risk. This study used a dynamic systems approach to examine the short-term temporal patterns between stress, hopelessness, suicide-specific rumination, and suicidal intent. Adults (N = 237; M = 27.12 years; 62% cisgender women; 87% White/European American) with elevated suicidal ideation completed ecological momentary assessments six times a day for 14 days. A multilevel model approach informed by dynamic systems theory was used to simultaneously assess stable and dynamic temporal processes underlying perceived stress, hopelessness, suicide-specific rumination, and suicidal intent. Each variable demonstrated temporal stability. In support of the cognitive model of suicide, we observed (1) a reciprocal relationship between stress and hopelessness such that stress and hopelessness amplified each other (early-stage processes), and (2) reinforcing loops such that hopelessness, suicide-specific rumination, and suicidal intent amplified each other (later-stage processes). A dynamic systems modeling approach underscored the negative impact of a perpetuating cycle of suicide-specific rumination, deepening hopelessness, and escalating suicidal intent on increasing suicide risk, which may be targets for intervention.

Differences in Lower-Extremity Joint Coordination During Two Landing Phases of a Drop Jump Task.

The aim of the present study was to compare the differences in joint coordination patterns and variability in the lower extremity between the first and second landing phases of the drop jump. Eighteen resistance-trained men (age: 22.8 ± 1.8 years) performed drop jumps from a height of 0.40 m. An eight-camera motion capture system was utilized to record kinematic trajectories. Modified vector coding technique and circular statistics were used to determine the coordination pattern and variability of the following joint couples during the first and second landings: hip frontal-knee frontal (HfKf), hip sagittal-knee frontal (HsKf), hip sagittal-knee sagittal (HsKs), knee frontal-ankle frontal (KfAf), knee sagittal-ankle frontal (KsAf), and knee sagittal-ankle sagittal (KsAs). Statistical differences in the distribution frequencies of coupling angles and variability between the dominant and nondominant limbs across the two landing phases were compared using two-way repeated analysis of variance and Wilcoxon rank-sum tests. During the second landing phase, the proportion of HsKs, KfAf, and KsAs showing in-phase coordination was reduced but the proportion of KfAf and KsAs showing proximal joint (knee) coordination was increased (p < .05). Significant differences in bilateral asymmetry were observed only for the HfKf and KfAf patients (p < .05). HsKs, KfAf, and KsAf varied considerably during the second landing phase (p < .05). Joint coordination patterns during the second landing phase of the drop jump differed considerably from those during the first landing phase, thereby increasing the risk of knee and ankle injuries.

Age-related differences in the interplay of fluency and complexity in Chinese-speaking seniors' oral narratives.

BACKGROUND: Previous studies on language production in normal ageing have primarily focused on distinct dimensions of older adults' spoken language performance, such as fluency and complexity. However, little attention has been paid to the complex, interconnected relations between these dimensions. Additionally, older adults have been treated as a homogeneous group, with little consideration for the differential characteristics of language performance across different stages of ageing. AIMS: This study aims to investigate how increasing age impacts Chinese seniors' oral language performance, focusing on fluency (articulation rate and dysfluency rate), complexity (lexical and syntactic) and the potential interactions between these dimensions. METHODS & PROCEDURES: Spontaneous oral narratives were collected from 60 normally ageing individuals, who were categorised into three groups: young-old (60-69 years old), middle-old (70-79 years old) and old-old (≥80 years old). Four measures for assessing language performance, namely, articulation rate, dysfluency rate, lexical complexity and syntactic complexity, were derived from the oral narratives. Dynamic systems techniques, including moving correlations, locally estimated scatterplot smoothing and Monte Carlo simulations, were employed for data analysis. OUTCOMES & RESULTS: This study revealed two major findings. First, across different age groups, the seniors' oral narratives significantly differed in the aspect of articulation rate and syntactic complexity. Specifically, both the young-old and the middle-old groups exhibited significantly higher articulation rates than the old-old group; the middle-old group also demonstrated significantly higher syntactic complexity compared to the old-old group. Second, the distinct subsystems (i.e., articulation rate, dysfluency rate, lexical and syntactic complexity) of seniors' oral narratives demonstrated varying interactions across different stages of ageing. While these subsystems tended to coordinate with each other in young-old individuals, they exhibited a greater tendency to compete in middle-old and old-old individuals. CONCLUSIONS & IMPLICATIONS: The findings reveal that subsystems of older adults' oral narratives display varying interactions with the increase of age, indicating that focusing solely on one dimension of language performance may result in inaccurate or misleading conclusions. Therefore, a multi-index comprehensive assessment should be employed for the enhancement of clinical evaluations of language performance in older adults. Additionally, it is vital to consider the interactional patterns (i.e., support or competition) between language subsystems when assessing language performance in normal ageing. WHAT THIS PAPER ADDS: What is already known on the subject Previous research on language production in normal ageing has primarily focused on distinct dimensions of older adults' spoken language performance, such as fluency, vocabulary richness and grammatical complexity, overlooking the intricate interconnections between these dimensions. However, investigating these connections is significant for a thorough and in-depth understanding of language production in late adulthood, which operates as a system comprising interconnected components. Furthermore, existing studies have predominantly treated older adults as a homogeneous group, comparing their language use to that of young people. This approach lacks justification, given the substantial variations in language use among seniors at different stages of ageing. What this paper adds to existing knowledge The present findings illuminate the dynamic nature of language production in normal ageing. Not only do the distinct dimensions or subsystems of older adults' oral narratives (e.g., articulation rate, dysfluency rate, syntactic complexity) change with age, but more important, the ways in which these subsystems interact with each other also evolve with age, resulting in changing states of spoken language production in the ageing process. More specifically, our study provides evidence that the interactions among subsystems of older adults' oral narratives predominately transition from supportive relationships in earlier stages to competitive ones at later stages of ageing. What are the potential or actual clinical implications of this work? The present study suggests the use of multiple linguistic indices related to various aspects of language performance to assess the spoken language production of older adults. This is highlighted by the observation that various dimensions of older adults' oral narratives continuously interact with one another across different stages of ageing, and hence focusing solely on one dimension of language performance tends to lead to inaccurate or misleading results. Additionally, the patterns of interaction (i.e., support or competition) between different dimensions of language performance may reflect older adults' capacity to coordinate and control attentional resources and therefore should be taken into consideration in clinical evaluations of language performance in older adults.

Repurposing the mammalian RNA-binding protein Musashi-1 as an allosteric translation repressor in bacteria.

The RNA recognition motif (RRM) is the most common RNA-binding protein domain identified in nature. However, RRM-containing proteins are only prevalent in eukaryotic phyla, in which they play central regulatory roles. Here, we engineered an orthogonal post-transcriptional control system of gene expression in the bacterium Escherichia coli with the mammalian RNA-binding protein Musashi-1, which is a stem cell marker with neurodevelopmental role that contains two canonical RRMs. In the circuit, Musashi-1 is regulated transcriptionally and works as an allosteric translation repressor thanks to a specific interaction with the N-terminal coding region of a messenger RNA and its structural plasticity to respond to fatty acids. We fully characterized the genetic system at the population and single-cell levels showing a significant fold change in reporter expression, and the underlying molecular mechanism by assessing the in vitro binding kinetics and in vivo functionality of a series of RNA mutants. The dynamic response of the system was well recapitulated by a bottom-up mathematical model. Moreover, we applied the post-transcriptional mechanism engineered with Musashi-1 to specifically regulate a gene within an operon, implement combinatorial regulation, and reduce protein expression noise. This work illustrates how RRM-based regulation can be adapted to simple organisms, thereby adding a new regulatory layer in prokaryotes for translation control.

The ecological dynamics of trumpet improvisation.

The nature of music improvisation continues to provide an interesting showcase of the multifaceted and skilful ways we engage with and act within our environments. Improvising musicians are somehow able to generate musical material in real time that adaptively navigates musical situations. In this article I explore the broader aspects of improvised activity-such as our bodily interactions with the instrument and environment-as they relate to improvised music-making. I do so by drawing upon principles from the embodied cognitive sciences, namely ecological and dynamical systems approaches. Firstly, I introduce the concept of affordances to illustrate the bidirectional relationship between improvisor and environment. I then take a dynamical view, exploring the ways that a trumpet player coordinates their body with their instrument and engages with trumpet affordances in order to navigate musical situations. I continue this dynamical view, taking the improviser to be an adaptive system whose behaviours are self-organised responses to a set of constraints. To conclude, I situate my research within the wider 4E approach. I advocate that 'E' approaches, which take seriously the role of the body-instrument-environment relationship, provide an insightful perspective on the nature of improvisation.

An investigation of the potential clinical utility of critical slowing down as an early warning sign for recurrence of depression.

BACKGROUND AND OBJECTIVES: Much of the burden of depressive illness is due to relapses that occur after treatment into remission. Prediction of an individual's imminent depressive relapse could lead to just-in-time interventions to prevent relapse, reducing depression's substantial burden of disability, costs, and suicide risk. Increasingly strong relationships in the form of autocorrelations between depressive symptoms, a signal of a phenomenon described as critical slowing down (CSD), have been proposed as a means of predicting relapse. METHODS: In the current study, four participants in remission from depression, one of whom relapsed, responded to daily smartphone surveys with depression symptoms. We used p-technique factor analysis to identify depression factors from over 100 survey responses. We then tested for the presence of CSD using time-varying vector autoregression and detrended fluctuation analysis. RESULTS: We found evidence that CSD provided an early warning sign for depression in the participant who relapsed, but we also detected false positive indications of CSD in participants who did not relapse. Results from time-varying vector autoregression and detrended fluctuation analysis were not in agreement. LIMITATIONS: Limitations include use of secondary data and a small number of participants with daily responding to a subset of depression symptoms. CONCLUSIONS: CSD provides a compelling framework for predicting depressive relapse and future research should focus on improving detection of early warning signs reliably. Improving early detection methods for depression is clinically significant, as it would allow for the development of just-in-time interventions.

The role of parenting in developmental trajectories of risk for adolescent substance use: a bioecological systems cascade model.

Parenting is a key influence and prevention target for adolescent substance use, and changes dramatically in form and function during adolescence. This theoretical synthesis reviews evidence of associations of substance use-specific parenting behaviors, dimensions, and styles with adolescent substance use, and integrates key developmental and family theories (e.g., bioecological, dynamical systems, family systems, developmental cascades) and methodological-conceptual advances to illustrate the complex role that parenting plays for the development of adolescent substance use in combination with child and contextual influences. The resulting bioecological systems cascade model centers the dynamic co-development of parenting and child influences in developmental cascades that lead to more or less risk for adolescent substance use. These trajectories are initiated by intergenerational influences, including genetics, parents' familial environments, and child-parent attachment. Culture and context influences are a holistic backdrop shaping parent-adolescent trajectories. Parenting is influences are conceptualized as a complex process by which specific parenting behaviors are informed by and accumulate into parenting dimensions which together comprise general parenting styles and are informed by the broader family context. The co-development of parenting and child biobehavioral risk is shaped by both parents and children, including by the genetics and environments they do and do not share. This co-development is dynamic, and developmental transitions of individuals and the family lead to periods of increased lability or variability that can change the longer-term trajectories of children's risk for substance use. Methodological avenues for future studies to operationalize the model are discussed.

COMPLEXITY MEASURES OF PSYCHOTIC BRAIN ACTIVITY IN THE FMRI SIGNAL.

When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain.

Features of Self-Organization during the Process of Mindfulness-Based Stress Reduction: A Single Case Study.

Compared to the extensive evidence of the effectiveness of mindfulness-based interventions, there is only a limited understanding of their mechanisms of change. The three aims of this study are (1) to identify features of self-organization during the process (e.g., pattern transitions), (2) to obtain an impression of the effects of continuous self-assessments and feedback sessions on mindfulness-related stress reduction, and (3) to test the feasibility of high-frequency process monitoring and process feedback. Concerning aim (1), the specific hypothesis is that change will occur as a cascade of discontinuous pattern transitions emerging spontaneously in the sense of not being a reaction to external input. This single case study describes changing patterns of multiple time series that were produced by app-based daily self-assessments during and after an 8-week mindfulness-based stress reduction program. After this MBSR program, the participant (a female nurse) continued the self-assessment and the mindfulness practice for a further 10 months. The results confirm findings on the positive effects of mindfulness programs for healthcare professionals, especially on coping with work-related stress. The analysis of the time series data supports the hypothesis of self-organization as a possible mechanism of change manifesting as a cascade of phase transitions in the dynamics of a biopsychosocial system. At the end of the year, the participant reported a beneficial impact of daily monitoring and systematic feedback on the change process. The results underline the feasibility and usefulness of continuous high-frequency monitoring during and after mindfulness interventions.

Dynamical Analyses Show That Professional Archers Exhibit Tighter, Finer and More Fluid Dynamical Control Than Neophytes.

Quantifying the dynamical features of discrete tasks is essential to understanding athletic performance for many sports that are not repetitive or cyclical. We compared three dynamical features of the (i) bow hand, (ii) drawing hand, and (iii) center of mass during a single bow-draw movement between professional and neophyte archers: dispersion (convex hull volume of their phase portraits), persistence (tendency to continue a trend as per Hurst exponents), and regularity (sample entropy). Although differences in the two groups are expected due to their differences in skill, our results demonstrate we can quantify these differences. The center of mass of professional athletes exhibits tighter movements compared to neophyte archers (6.3 < 11.2 convex hull volume), which are nevertheless less persistent (0.82 < 0.86 Hurst exponent) and less regular (0.035 > 0.025 sample entropy). In particular, the movements of the bow hand and center of mass differed more between groups in Hurst exponent analysis, and the drawing hand and center of mass were more different in sample entropy analysis. This suggests tighter neuromuscular control over the more fluid dynamics of the movement that exhibits more active corrections that are more individualized. Our work, therefore, provides proof of principle of how well-established dynamical analysis techniques can be used to quantify the nature and features of neuromuscular expertise for discrete movements in elite athletes.

A Multivariate Method for Dynamic System Analysis: Multivariate Detrended Fluctuation Analysis Using Generalized Variance.

Fractal fluctuations are a core concept for inquiries into human behavior and cognition from a dynamic systems perspective. Here, we present a generalized variance method for multivariate detrended fluctuation analysis (mvDFA). The advantage of this extension is that it can be applied to multivariate time series and considers intercorrelation between these time series when estimating fractal properties. First, we briefly describe how fractal fluctuations have advanced a dynamic system understanding of cognition. Then, we describe mvDFA in detail and highlight some of the advantages of the approach for simulated data. Furthermore, we show how mvDFA can be used to investigate empirical multivariate data using electroencephalographic recordings during a time-estimation task. We discuss this methodological development within the framework of interaction-dominant dynamics. Moreover, we outline how the availability of multivariate analyses can inform theoretical developments in the area of dynamic systems in human behavior.

Motorized Photomodulator: Making A Non-photoresponsive Supramolecular Gel Switchable by Light.

Introducing photo-responsive molecules offers an attractive approach for remote and selective control and dynamic manipulation of material properties. However, it remains highly challenging how to use a minimal amount of photo-responsive units to optically modulate materials that are inherently inert to light irradiation. Here we show the application of a light-driven rotary molecular motor as a "motorized photo-modulator" to endow a typical H-bond-based gel system with the ability to respond to light irradiation and create a reversible sol-gel transition. The key molecular design feature is the introduction of a minimal amount (2 mol %) of molecular motors into the supramolecular network as photo-switchable non-covalent crosslinkers. Advantage is taken of the subtle interplay of the large geometry change during photo-isomerization of the molecular motor guest and the dynamic nature of a supramolecular gel host system. As a result, a tiny amount of molecular motors is enough to switch the mechanical modulus of the entire supramolecular systems. This study proves the concept of designing photo-responsive materials with minimum use of non-covalent light-absorbing units.

Temporal associations between salivary cortisol and emotions in clinically depressed individuals and matched controls: A dynamic time warp analysis.

Depression can be understood as a complex dynamic system where depressive symptoms interact with one another. Cortisol is suggested to play a major role in the pathophysiology of depression, but knowledge on the temporal interplay between cortisol and depressive symptoms is scarce. We aimed to analyze the temporal connectivity between salivary cortisol and momentary affective states in depressed individuals and controls. Thirty pair-matched depressed and non-depressed participants completed questionnaires on momentary positive (PA) and negative (NA) affect and collected saliva three times a day for 30 days. The association between cortisol and affect was analyzed by dynamic time warp (DTW) analyses. These analyses involved lag-1 backward to lag-1 forward undirected analyses and lag-0 and lag-1 forward directed analyses. Large inter- and intra-individual variability in the networks were found. At the group level, with undirected analysis PA and NA were connected in the networks in depressed individuals and in controls. Directed analyses indicated that increases in cortisol preceded specific NA items in controls, but tended to follow upon specific affect items increase in depressed individuals. To conclude, at group level, changes in cortisol levels in individuals diagnosed with a depression may be a result of changes in affect, rather than a cause.

Dynamic regulatory processes among child welfare parents: Temporal associations between physiology and parenting behavior.

This study examined how temporal associations between parents' physiological and behavioral responses may reflect underlying regulatory difficulties in at-risk parenting. Time-series data of cardiac indices (second-by-second estimates of inter-beat intervals - IBI, and respiratory sinus arrhythmia - RSA) and parenting behaviors were obtained from 204 child welfare-involved parents (88% mothers, Mage = 32.32 years) during child-led play with their 3- to 7-year-old children (45.1% female; Mage = 4.76 years). Known risk factors for maltreatment, including parents' negative social cognitions, mental health symptoms, and inhibitory control problems, were examined as moderators of intra-individual physiology-behavior associations. Results of ordinary differential equations suggested increases in parents' cardiac arousal at moments when they showed positive parenting behaviors. In turn, higher arousal was associated with momentary decreases in both positive and negative parenting behaviors. Individual differences in these dynamic processes were identified in association with parental risk factors. In contrast, no sample-wide RSA-behavior associations were evident, but a pattern of increased positive parenting at moments of parasympathetic withdrawal emerged among parents showing more total positive parenting behaviors. This study illustrated an innovative and ecologically-valid approach to examining regulatory patterns that may shape parenting in real-time and identified mechanisms that should be addressed in interventions.