Search

Objective. This multimethod, prospective study examined whether child and parent gender moderate the relation between interparental discord and changes in parenting practices over a 1-year time span. Design. Self-report questionnaires of interparental discord and parenting as well as an observational measure of interparental discord were collected from a sample of 226 mothers and fathers of kindergarten children. Results. Multigroup analyses using structural equation models indicated that the relation between a multimethod assessment of interparental discord and changes in parent-reported childrearing practices varied significantly as a function of child gender only. Interparental discord predicted decreases in parental responsiveness to boys' distress while predicting increases in parental responsiveness to girls' distress. Moderating analyses further indicated that interparental discord predicted significant increases in parental use of psychological control with boys only. Conclusions. The considerable heterogeneity in previous research examining prospective linkages between interparental discord and parenting practices may be attributable, in part, to the moderating role of child gender. Differences in the moderating role of child gender across separate dimensions of parenting highlight the importance of distinguishing among specific parenting practices in examining gender differences in family pathways. [ABSTRACT FROM AUTHOR]

Empathy requires the ability to understand another's point of view and is critical for motivating a person to help others. However, little is known about the link between experiences of empathic emotional engagement in close friendships during adolescence and neural correlates of empathy in adulthood. Beginning in 1998, N  = 88 participants drawn from a demographically diverse community sample were observed annually from ages 13 to 21 and rated on the amount of emotional engagement displayed toward a close friend during a support task. At approximately age 24, participants underwent functional brain imaging while a partner or stranger was under distress. Contrary to predictions, greater emotional engagement with close friends during adolescence corresponded prospectively with reduced temporal pole activity (a region associated with cognitive empathy and perspective taking) while observing threats directed at others. Results have implications for understanding the neurodevelopmental roots of empathy.

Past work suggested that psychological stress, especially in the context of relationship stress, is associated with increased consumption of energy-dense food and when maintained for long periods of time, leads to adverse health consequences. Furthermore, this association is moderated by a variety of factors, including emotional over-eating style. That being said, few work utilized a dynamical system approach to understand the intraindividual and interindividual fluctuations within this process. The current study utilized a 14-day daily diary study, collected between January-March 2020, where participants reported their partner's negative relationship behavior and their own snacking behavior. A differential equation model was applied to the daily dairy data collected. Results showed that snacking behavior followed an undamped oscillator model while negative relationship behavior followed a damped coupled oscillator model. In other words, snacking behavior fluctuated around an equilibrium but was not coupled within dyadic partners. Negative relationship behavior fluctuated around an equilibrium and was amplified over time, coupled within dyadic partners. Furthermore, we found a two-fold association between negative relationship behavior and snacking: while the association between the displacement of negative relationship behavior and snacking was negative, change in negative relationship behavior and snacking were aligned. Thus, at any given time, one's snacking depends both on the amount of negative relationship behaviors one perceives and the dynamical state a dyad is engaging in (i.e., whether the negative relationship behavior is "exacerbating" or "resolving"). This former association was moderated by emotional over-eating style and the latter association was not. The current findings highlight the importance of examining dynamics within dyadic system and offers empirical and methodological insights for research in adult relationships., Competing Interests: Declaration of competing interest We have no financial or personal relationships with other people or organizations that could inappropriately influence (bias) our work to disclose., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Methods that measure the association between two intensively measured time series are of interest to researchers studying the symmetry of behaviors during social interaction. Such methods have historically focused on aggregating the amount of symmetry across all measurement occasions. However, it is rarely expected that symmetry is present at all measurement occasions. The current method, the pairwise approximate spatiotemporal symmetry (PASS) algorithm, is an approach that may be used to determine which measurement occasions in pairwise time series are indicative of symmetry and which are not. This process thus divides time series into symmetric and nonsymmetric segments. The PASS algorithm is demonstrated here on representative simulated data and naturalistic psychotherapy data. Results suggest that the PASS algorithm has the potential to extract meaningful symmetry segments from human signals. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Anxiety disorders are highly prevalent, and rates increased during the COVID-19 pandemic. However, most individuals with elevated anxiety do not access treatment due to barriers such as stigma, cost, and availability. Digital mental health programs, such as cognitive bias modification for interpretation (CBM-I), hold promise in increasing access to care. Before widely disseminating CBM-I, we must rigorously test its effectiveness and determine whom it is best positioned to benefit. The present study (which is a substudy of a parent trial) compared CBM-I against psychoeducation offered through the public website MindTrails, and also tested whether baseline anxiety tied to COVID-19 influenced the rate of change in anxiety and interpretation bias during and after each intervention. Adults with moderate-to-severe anxiety symptoms were randomly assigned to complete five sessions of either CBM-I or psychoeducation as part of a larger trial, and 608 enrolled in this substudy after Session 1. As predicted (https://osf.io/2dyzr), CBM-I was superior to psychoeducation at reducing anxiety symptoms (on the OASIS but not the DASS-21-AS: d = -0.31), reducing negative interpretation bias (d range = -0.34 to -0.43), and increasing positive interpretation bias (d = 0.79) by the end of treatment. Results also indicated that individuals higher (vs. lower) in baseline COVID-19 anxiety had stronger decreases in anxiety symptoms while receiving CBM-I but weaker decreases in anxiety symptoms (on the DASS-21-AS) while receiving psychoeducation. These findings suggest that CBM-I may be a useful anxiety-reduction tool for individuals experiencing higher anxiety tied to uncertain events such as the COVID-19 pandemic., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Interpersonal neural synchrony (INS) occurs when neural electrical activity temporally aligns between individuals during social interactions. It has been used as a metric for interpersonal closeness, often during naturalistic child-parent interactions. This study evaluated whether other biological correlates of social processing predicted the prevalence of INS during child-parent interactions, and whether their observed cooperativity modulated this association. Child-parent dyads (n = 27) performed a visuospatial tower-building task in cooperative and competitive conditions. Neural activity was recorded using mobile electroencephalogram (EEG) headsets, and experimenters coded video-recordings post-hoc for behavioral attunement. DNA methylation of the oxytocin receptor gene (OXTRm) was measured, an epigenetic modification associated with reduced oxytocin activity and socioemotional functioning. Greater INS during competition was associated with lower child OXTRm, while greater behavioral attunement during competition and cooperation was associated with higher parent OXTRm. These differential relationships suggest that interpersonal dynamics as measured by INS may be similarly reflected by other biological markers of social functioning, irrespective of observed behavior. Children's self-perceived communication skill also showed opposite associations with parent and child OXTRm, suggesting complex relationships between children's and their parents' social functioning. Our findings have implications for ongoing developmental research, supporting the utility of biological metrics in characterizing interpersonal relationships., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

We present a novel method for quantifying transitions within multivariate binary time series data, using a sliding series of transition matrices, to derive metrics of stability and spread. We define stability as the trace of a transition matrix divided by the sum of all observed elements within that matrix. We define spread as the number of all non-zero cells in a transition matrix divided by the number of all possible cells in that matrix. We developed this method to allow investigation into high-dimensional, sparse data matrices for which existing binary time series methods are not designed. Results from 1728 simulations varying six parameters suggest that unique information is captured by both metrics, and that stability and spread values have a moderate inverse association. Further, simulations suggest that this method can be reliably applied to time series with as few as nine observations per person, where at least five consecutive observations construct each overlapping transition matrix, and at least four time series variables compose each transition matrix. A pre-registered application of this method using 4 weeks of ecological momentary assessment data (N = 110) showed that stability and spread in the use of 20 emotion regulation strategies predict next timepoint affect after accounting for affect and anxiety's auto-regressive and cross-lagged effects. Stability, but not spread, also predicted next timepoint anxiety. This method shows promise for meaningfully quantifying two unique aspects of switching behavior in multivariate binary time series data., (© 2022. The Psychonomic Society, Inc.)

Self-regulating systems change along different timescales. Within a given week, a depressed person's affect might oscillate around a low equilibrium point. However, when the timeframe is expanded to capture the year during which they onboarded antidepressant medication, their equilibrium and oscillatory patterns might reorganize around a higher affective point. To simultaneously account for the meaningful change processes that happen at different time scales in complex self-regulatory systems, we propose a single model that combines a second-order linear differential equation for short timescale regulation and a first-order linear differential equation for long timescale adaptation of equilibrium. This model allows for individual-level moderation of short-timescale model parameters. The model is tested in a simulation study which shows that, surprisingly, the short and long timescales can fully overlap and the model still converges to the reasonable estimates. Finally, an application of this model to self-regulation of emotional well-being in recent widows is presented and discussed.

Analytical methods derived from nonlinear dynamical systems, complexity, and chaos theories offer researchers a framework for in-depth analysis of time series data. However, relatively few studies involving time series data obtained from psychological and behavioral research employ such methods. This paucity of application is due to a lack of general analysis frameworks for modeling time series data with strong nonlinear components. In this article, we describe the potential of Hankel alternative view of Koopman (HAVOK) analysis for solving this issue. HAVOK analysis is a unified framework for nonlinear dynamical systems analysis of time series data. By utilizing HAVOK analysis, researchers may model nonlinear time series data in a linear framework while simultaneously reconstructing attractor manifolds and obtaining a secondary time series representing the amount of nonlinear forcing occurring in a system at any given time. We begin by showing the mathematical underpinnings of HAVOK analysis and then show example applications of HAVOK analysis for modeling time series data derived from real psychological and behavioral studies.

The past few years were marked by increased online offensive strategies perpetrated by state and non-state actors to promote their political agenda, sow discord, and question the legitimacy of democratic institutions in the US and Western Europe. In 2016, the US congress identified a list of Russian state-sponsored Twitter accounts that were used to try to divide voters on a wide range of issues. Previous research used latent Dirichlet allocation (LDA) to estimate latent topics in data extracted from these accounts. However, LDA has characteristics that may limit the effectiveness of its use on data from social media: The number of latent topics must be specified by the user, interpretability of the topics can be difficult to achieve, and it does not model short-term temporal dynamics. In the current paper, we propose a new method to estimate latent topics in texts from social media termed Dynamic Exploratory Graph Analysis (DynEGA). In a Monte Carlo simulation, we compared the ability of DynEGA and LDA to estimate the number of simulated latent topics. The results show that DynEGA is substantially more accurate than several different LDA algorithms when estimating the number of simulated topics. In an applied example, we performed DynEGA on a large dataset with Twitter posts from state-sponsored right- and left-wing trolls during the 2016 US presidential election. DynEGA revealed topics that were pertinent to several consequential events in the election cycle, demonstrating the coordinated effort of trolls capitalizing on current events in the USA. This example demonstrates the potential power of our approach for revealing temporally relevant information from qualitative text data., (© 2021. The Author(s).)

Temporal complexity refers to qualities of a time series that are emergent, erratic, or not easily described by linear processes. Quantifying temporal complexity within a system is key to understanding the time based dynamics of said system. However, many current methods of complexity quantification are not widely used in psychological research because of their technical difficulty, computational intensity, or large number of required data samples. These requirements impede the study of complexity in many areas of psychological science. A method is presented, tangle, which overcomes these difficulties and allows for complexity quantification in relatively short time series, such as those typically obtained from psychological studies. Tangle is a measure of how dissimilar a given process is from simple periodic motion. Tangle relies on the use of a three-dimensional time delay embedding of a one-dimensional time series. This embedding is then iteratively scaled and premultiplied by a modified upshift matrix until a convergence criterion is reached. The efficacy of tangle is shown on five mathematical time series and using emotional stability, anxiety time series data obtained from 65 socially anxious participants over a 5-week period, and positive affect time series derived from a single participant who experienced a major depression episode during measurement. Simulation results show tangle is able to distinguish between different complex temporal systems in time series with as few as 50 samples. Tangle shows promise as a reliable quantification of irregular behavior of a time series. Unlike many other complexity quantification metrics, tangle is technically simple to implement and is able to uncover meaningful information about time series derived from psychological research studies. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Conventional longitudinal behavioral genetic models estimate the relative contribution of genetic and environmental factors to stability and change of traits and behaviors. Longitudinal models rarely explain the processes that generate observed differences between genetically and socially related individuals. We propose that exchanges between individuals and their environments (i.e., phenotype-environment effects) can explain the emergence of observed differences over time. Phenotype-environment models, however, would require violation of the independence assumption of standard behavioral genetic models; that is, uncorrelated genetic and environmental factors. We review how specification of phenotype-environment effects contributes to understanding observed changes in genetic variability over time and longitudinal correlations among nonshared environmental factors. We then provide an example using 30 days of positive and negative affect scores from an all-female sample of twins. Results demonstrate that the phenotype-environment effects explain how heritability estimates fluctuate as well as how nonshared environmental factors persist over time. We discuss possible mechanisms underlying change in gene-environment correlation over time, the advantages and challenges of including gene-environment correlation in longitudinal twin models, and recommendations for future research.

The accurate identification of the content and number of latent factors underlying multivariate data is an important endeavor in many areas of Psychology and related fields. Recently, a new dimensionality assessment technique based on network psychometrics was proposed (Exploratory Graph Analysis, EGA), but a measure to check the fit of the dimensionality structure to the data estimated via EGA is still lacking. Although traditional factor-analytic fit measures are widespread, recent research has identified limitations for their effectiveness in categorical variables. Here, we propose three new fit measures (termed entropy fit indices) that combines information theory, quantum information theory and structural analysis: Entropy Fit Index (EFI), EFI with Von Neumman Entropy (EFI.vn) and Total EFI.vn (TEFI.vn). The first can be estimated in complete datasets using Shannon entropy, while EFI.vn and TEFI.vn can be estimated in correlation matrices using quantum information metrics. We show, through several simulations, that TEFI.vn, EFI.vn and EFI are as accurate or more accurate than traditional fit measures when identifying the number of simulated latent factors. However, in conditions where more factors are extracted than the number of factors simulated, only TEFI.vn presents a very high accuracy. In addition, we provide an applied example that demonstrates how the new fit measures can be used with a real-world dataset, using exploratory graph analysis.

Although many studies have unequivocally demonstrated the promise of understanding resilience to adversity and characterizing the consequences if stress is unabated, needed are dynamic theories and methods to enhance the rigor and interpretation of these assessments. From a dynamic systems perspective, the focus is not whether an individual possesses some fixed ability or unchangeable trait, but rather to understand the flexibility and responsiveness of stress regulation systems to daily hassles and adverse life events. A renewed interest in individual variability allows researchers to see trajectories of change over both short- and long-time scales to understand the developmental course. As a result, it is possible to answer questions, such as, how does the dysregulation in emotion caused by stress, to both within and between daily affect processes, relate to longitudinal trajectories (over time-scales of years) of dysfunction and disease? The overarching goal of the Notre Dame Study of Health & Well-being is to detail the types and qualities of contextual influences, in conjunction with dynamic psychobiological systems, to assess the precursors, concomitant influences and consequences of stress and resilience in the face of adversity on cognitive, health and well-being outcomes.

Constrained fourth-order latent differential equation (FOLDE) models have been proposed (e.g., Boker et al. 2020) as alternative to second-order latent differential equation (SOLDE) models to estimate second-order linear differential equation systems such as the damped linear oscillator model. When, however, only a relatively small number of measurement occasions T are available (i.e., [Formula: see text]), the recommendation of which model to use is not clear (Boker et al. 2020). Based on a data set, which consists of [Formula: see text] observations of daily stress for [Formula: see text] individuals, we illustrate that FOLDE can help to choose an embedding dimension, even in the case of a small T. This is of great importance, as parameter estimates depend on the embedding dimension as well as on the latent differential equations model. Consequently, the wavelength as quantity of potential substantive interest may vary considerably. We extend the modeling approaches used in past research by including multiple subjects, by accounting for individual differences in equilibrium, and by including multiple instead of one single observed indicator.

Cooperation is pivotal for society to flourish. To foster cooperation, humans express and read intentions via explicit signals and subtle reflections of arousal visible in the face. Evidence is accumulating that humans synchronize these nonverbal expressions and the physiological mechanisms underlying them, potentially influencing cooperation. The current study is designed to verify this putative linkage between synchrony and cooperation. To that end, 152 participants played the Prisoner's Dilemma game in a dyadic interaction setting, sometimes facing each other and sometimes not. Results showed that synchrony in both heart rate and skin conductance level emerged during face-to-face contact. However, only synchrony in skin conductance levels predicted cooperative success of dyads. Crucially, this positive linkage was strengthened when participants could see each other. These findings show the strong relationship between our bodily responses and social behavior, and emphasize the importance of studying social processes between rather than within individuals in real-life interactions.

Objective: One of the promises of the experience sampling methodology (ESM) is that a statistical analysis of an individual's emotions, cognitions and behaviors in everyday-life could be used to identify relevant treatment targets. A requisite for clinical implementation is that outcomes of such person-specific time-series analyses are not wholly contingent on the researcher performing them., Methods: To evaluate this, we crowdsourced the analysis of one individual patient's ESM data to 12 prominent research teams, asking them what symptom(s) they would advise the treating clinician to target in subsequent treatment., Results: Variation was evident at different stages of the analysis, from preprocessing steps (e.g., variable selection, clustering, handling of missing data) to the type of statistics and rationale for selecting targets. Most teams did include a type of vector autoregressive model, examining relations between symptoms over time. Although most teams were confident their selected targets would provide useful information to the clinician, not one recommendation was similar: both the number (0-16) and nature of selected targets varied widely., Conclusion: This study makes transparent that the selection of treatment targets based on personalized models using ESM data is currently highly conditional on subjective analytical choices and highlights key conceptual and methodological issues that need to be addressed in moving towards clinical implementation., Competing Interests: Declaration of Competing Interest The authors declared no conflicts of interest with respect to the authorship or the publication of this article., (Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.)

Second order linear differential equations can be used as models for regulation since under a range of parameter values they can account for return to equilibrium as well as potential oscillations in regulated variables. One method that can estimate parameters of these equations from intensive time series data is the method of Latent Differential Equations (LDE). However, the LDE method can exhibit bias in its parameters if the dimension of the time delay embedding and thus the width of the convolution kernel is not chosen wisely. This article presents a simulation study showing that a constrained fourth order Latent Differential Equation (FOLDE) model for the second order system almost completely eliminates bias as long as the width of the convolution kernel is less than two thirds the period of oscillations in the data. The FOLDE model adds two degrees of freedom over the standard LDE model but significantly improves model fit.

Which is more enjoyable: trying to think enjoyable thoughts or doing everyday solitary activities? Wilson et al. (2014) found that American participants much preferred solitary everyday activities, such as reading or watching TV, to thinking for pleasure. To see whether this preference generalized outside of the United States, we replicated the study with 2,557 participants from 12 sites in 11 countries. The results were consistent in every country: Participants randomly assigned to do something reported significantly greater enjoyment than did participants randomly assigned to think for pleasure. Although we found systematic differences by country in how much participants enjoyed thinking for pleasure, we used a series of nested structural equation models to show that these differences were fully accounted for by country-level variation in 5 individual differences, 4 of which were positively correlated with thinking for pleasure (need for cognition, openness to experience, meditation experience, and initial positive affect) and 1 of which was negatively correlated (reported phone usage). (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Objective: The aim of this study was to evaluate the diagnostic performance of susceptibility-weighted magnetic resonance imaging (SW-MRI) for the evaluation of osseous foraminal stenosis (FS) of the cervical spine compared to conventional MRI-sequences, using computed tomography (CT) as a reference standard., Materials and Methods: Twenty-one patients with suspected radiculopathy of the cervical spine were prospectively included. CT and MRI data sets were available for all patients. As standard of reference, 280 neuroforamina of the cervical spine, including 58 foraminal stenosis, were identified on sagittal CT images. T1-, T2-, and SW-MRI of the cervical spine were performed. The presence of foraminal stenosis was assessed on sagittal views in all sequences. Sensitivity and specificity were calculated and differences in detection rate and severity scoring of foraminal stenosis between the different sequences were tested. CT was used as reference standard for all analysis., Results: Fifty-six of 58 osseous foraminal stenosis could be correctly identified on SW-MR magnitude images. SW-MRI achieved a sensitivity of 96.6% and specificity of 99.5% for the identification of foraminal stenosis. In comparison, conventional T1-weighted MRI sequences achieved a sensitivity and specificity of 43.1% and 100% respectively. T2-weighted MRI sequences achieved a sensitivity and specificity of 65.5% and 99.1%, respectively. The overall detection rate was significantly (p < 0.05) higher on SW-MRI and there was no significant difference (p > 0.05) in severity scoring compared to CT. T1- and T2-weighted MRI underestimated the degree of foraminal stenosis. Intermodality and interobserver agreements were highest for SW-MRI., Conclusions: SW-MRI enables the reliable detection of osseous foraminal stenosis of the cervical spine in patients with spinal radiculopathy with a higher sensitivity compared to conventional T1- and T2-MRI sequences, with CT as a reference standard., Key Points: • Susceptibility-weighted magnetic resonance imaging enables the reliable detection of osseous foraminal stenosis of the cervical spine with CT as a reference standard. • This could be relevant for younger patients in order to prevent unnecessary radiation exposure. • This may also facilitate a one-stop-shop approach and speed up diagnostic work-up.

Synchrony between interacting systems is an important area of nonlinear dynamics in physical systems. Recently psychological researchers from multiple areas of psychology have become interested in nonverbal synchrony (i.e., coordinated motion between two individuals engaged in dyadic information exchange such as communication or dance) as a predictor and outcome of psychological processes. An important step in studying nonverbal synchrony is systematically and validly differentiating synchronous systems from nonsynchronous systems. However, many current methods of testing and quantifying nonverbal synchrony will show some level of observed synchrony even when research participants have not interacted with one another. In this article we demonstrate the use of surrogate data generation methodology as a means of testing new null-hypotheses for synchrony between bivariate time series such as those derived from modern motion tracking methods. Hypotheses generated by surrogate data generation methods are more nuanced and meaningful than hypotheses from standard null-hypothesis testing. We review four surrogate data generation methods for testing for significant nonverbal synchrony within a windowed cross-correlation (WCC) framework. We also interpret the null-hypotheses generated by these surrogate data generation methods with respect to nonverbal synchrony as a specific use of surrogate data generation, which can then be generalized for hypothesis testing of other psychological time series. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The 10 year anniversary of the COGITO Study provides an opportunity to revisit the ideas behind the Cattell data box. Three dimensions of the persons × variables × time data box are discussed in the context of three categories of researchers each wanting to answer their own categorically different question. The example of the well-known speed-accuracy tradeoff is used to illustrate why these are three different categories of statistical question. The 200 persons by 100 variables by 100 occasions of measurement COGITO data cube presents a challenge to integrate theories and methods across the dimensions of the data box. A conceptual model is presented for the speed-accuracy tradeoff example that could account for cross-sectional between persons effects, short-term dynamics, and long-term learning effects. Thus, two fundamental differences between the time axis and the other two axes of the data box include ordering and time scaling. In addition, nonstationarity in human systems is a pervasive problem along the time dimension of the data box. To illustrate, the difference in nonstationarity between dancing and conversation is discussed in the context of the interaction between theory, methods, and data. An information theoretic argument is presented that the theory-methods-data interaction is better understood when viewed as a conversation than as a dance. Entropy changes in the development of a theory-methods-data conversation provide one metric for evaluating scientific progress.

Damped Linear Oscillators estimated by 2nd-order Latent Differential Equation have assumed a constant equilibrium and one oscillatory component. Lower-frequency oscillations may come from seasonal background processes, which non-randomly contribute to deviation from equilibrium at each occasion and confound estimation of dynamics over shorter timescales. Boker (2015) proposed a model of individual change on multiple timescales, but implementation, simulation, and applications to data have not been demonstrated. This study implemented a generalization of the proposed model; examined robustness to varied timescale ratios, measurement error, and occasions-per-person in simulated data; and tested for dynamics at multiple timescales in experience sampling affect data. Results show small standard errors and low bias to dynamic estimates at timescale ratios greater than 3:1. Below 3:1, estimate error was sensitive to noise and total occasions; rates of non-convergence increased. For affect data, model comparisons showed statistically significant dynamics at both timescales for both participants.

Recent investigations highlighted the role of within-person pubertal changes for adolescents' behavior. Yet, little is known about effects on adolescents' daily affect, particularly regarding the hormonal changes underlying physical changes during puberty. In a study with 148 boys aged 10 to 20years, we tested whether within-person physical and hormonal changes over eight months predicted everyday affect fluctuations, measured with experience sampling. As expected, greater within-person changes in testosterone (but not in dehydroepiandrosterone) were associated with higher affect fluctuations in daily life. Additionally, greater physical changes predicted higher affect fluctuations for individuals in the beginning of puberty. The findings demonstrate the relevance of physical and hormonal changes in boys' affective (in)stability., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Structural equation models are increasingly used for clustered or multilevel data in cases where mixed regression is too inflexible. However, when there are many levels of nesting, these models can become difficult to estimate. We introduce a novel evaluation strategy, Rampart, that applies an orthogonal rotation to the parts of a model that conform to commonly met requirements. This rotation dramatically simplifies fit evaluation in a way that becomes more potent as the size of the data set increases. We validate and evaluate the implementation using a 3-level latent regression simulation study. Then we analyze data from a state-wide child behavioral health measure administered by the Oklahoma Department of Human Services. We demonstrate the efficiency of Rampart compared to other similar software using a latent factor model with a 5-level decomposition of latent variance. Rampart is implemented in OpenMx, a free and open source software.

The new software package OpenMx 2.0 for structural equation and other statistical modeling is introduced and its features are described. OpenMx is evolving in a modular direction and now allows a mix-and-match computational approach that separates model expectations from fit functions and optimizers. Major backend architectural improvements include a move to swappable open-source optimizers such as the newly written CSOLNP. Entire new methodologies such as item factor analysis and state space modeling have been implemented. New model expectation functions including support for the expression of models in LISREL syntax and a simplified multigroup expectation function are available. Ease-of-use improvements include helper functions to standardize model parameters and compute their Jacobian-based standard errors, access to model components through standard R $ mechanisms, and improved tab completion from within the R Graphical User Interface.

Reliability has a long history as one of the key psychometric properties of a test. However, a given test might not measure people equally reliably. Test scores from some individuals may have considerably greater error than others. This study proposed two approaches using intraindividual variation to estimate test reliability for each person. A simulation study suggested that the parallel tests approach and the structural equation modeling approach recovered the simulated reliability coefficients. Then in an empirical study, where forty-five females were measured daily on the Positive and Negative Affect Schedule (PANAS) for 45 consecutive days, separate estimates of reliability were generated for each person. Results showed that reliability estimates of the PANAS varied substantially from person to person. The methods provided in this article apply to tests measuring changeable attributes and require repeated measures across time on each individual. This article also provides a set of parallel forms of PANAS.

A framework is presented for building and testing models of dynamic regulation by categorizing sources of differences between theories of dynamics. A distinction is made between the dynamics of change, i.e., how a system self-regulates on a short time scale, and change in dynamics, i.e., how those dynamics may themselves change over a longer time scale. In order to clarify the categories, models are first built to estimate individual differences in equilibrium value and equilibrium change. Next, models are presented in which there are individual differences in parameters of dynamics such as frequency of fluctuations, damping of fluctuations, and amplitude of fluctuations. Finally, models for within-person change in dynamics over time are proposed. Simulations demonstrating feasibility of these models are presented and OpenMx scripts for fitting these models have been made available in a downloadable archive along with scripts to simulate data so that a researcher may test a selected models' feasibility within a chosen experimental design.

The coordination of physiological processes between parents and infants is thought to support behaviors critical for infant adaptation, but we know little about parent-child physiological coregulation during the preschool years. The present study examined whether time-varying changes in parent and child respiratory sinus arrhythmia (RSA) exhibited coregulation (across-person dynamics) accounting for individual differences in parent and child RSA, and whether there were differences in these parasympathetic processes by children's externalizing problems. Mother-child dyads (N = 47; Child age M = 3½ years) engaged in three laboratory tasks (free play, clean up, puzzle task) for 18 min, during which RSA data were collected. Multilevel coupled autoregressive models revealed that mothers and preschoolers showed positive coregulation of RSA such that changes in mother RSA predicted changes in the same direction in child RSA and vice versa, controlling for the stability of within-person RSA over time and individual differences in overall mean RSA. However, when children's externalizing behaviors were higher, coregulation was negative such that changes in real-time mother and child RSA showed divergence rather than positive concordance. Results suggest that mothers and preschoolers do coregulate RSA during real-time interactions, but that children's higher externalizing behavior problems are related to disruptions in these processes., (© 2015 Wiley Periodicals, Inc.)

An equilibrium involves a balancing of forces. Just as one maintains upright posture in standing or walking, many self-regulatory and interpersonal behaviors can be framed as a balancing act between an ever changing environment and within-person processes. The emerging balance between person and environment, the equilibria, are dynamic and adaptive in response to development and learning. A distinction is made between equilibrium achieved solely due to a short timescale balancing of forces and a longer timescale preferred equilibrium which we define as a state towards which the system slowly adapts. Together, these are developed into a framework that this article calls Adaptive Equilibrium Regulation (ÆR), which separates a regulatory process into two timescales: a faster regulation that automatically balances forces and a slower timescale adaptation process that reconfigures the fast regulation so as to move the system towards its preferred equilibrium when an environmental force persists over the longer timescale. This way of thinking leads to novel models for the interplay between multiple timescales of behavior, learning, and development.

Prenatal testosterone exposure may be protective against disordered eating. However, prior studies have produced mixed results. Developmental differences in prenatal testosterone's protective effects on disordered eating may explain these discrepancies. Indeed, studies have differed in the age of participants assessed, with data supporting prenatal testosterone effects on disordered eating in early adolescent and young adult samples but not in late adolescence. The present studies are the first to investigate age differences in prenatal testosterone's protective effects on disordered eating. Two indirect markers of higher prenatal testosterone were examined: (a) lower finger-length ratios (Study 1: index [2D]/ring [4D] finger [2D:4D]) and (b) lower disordered eating in female s from opposite-sex twin pairs (who are thought to be exposed to higher prenatal testosterone from their male co-twin) relative to female controls (Study 2). Participants were twins from the Michigan State University Twin Registry (Study 1: n = 409; Study 2: n = 1,538) in early adolescence, late adolescence, or young adulthood. Disordered eating was assessed with well-validated questionnaires. Finger-length ratios were measured from hand scans, using electronic computer calipers. Findings were consistent across both studies. Higher prenatal testosterone (lower 2D:4D; females from opposite-sex twin pairs vs. controls) predicted lower disordered eating in early adolescence and young adulthood only. Prenatal testosterone-disordered eating associations were not observed during late adolescence. Results point to the possibility of developmental windows of expression for prenatal testosterone's protective effects on disordered eating and suggest that prior discrepant results may reflect age differences across samples., (PsycINFO Database Record (c) 2015 APA, all rights reserved.)

Maintained Individual Data Distributed Likelihood Estimation (MIDDLE) is a novel paradigm for research in the behavioral, social, and health sciences. The MIDDLE approach is based on the seemingly impossible idea that data can be privately maintained by participants and never revealed to researchers, while still enabling statistical models to be fit and scientific hypotheses tested. MIDDLE rests on the assumption that participant data should belong to, be controlled by, and remain in the possession of the participants themselves. Distributed likelihood estimation refers to fitting statistical models by sending an objective function and vector of parameters to each participant's personal device (e.g., smartphone, tablet, computer), where the likelihood of that individual's data is calculated locally. Only the likelihood value is returned to the central optimizer. The optimizer aggregates likelihood values from responding participants and chooses new vectors of parameters until the model converges. A MIDDLE study provides significantly greater privacy for participants, automatic management of opt-in and opt-out consent, lower cost for the researcher and funding institute, and faster determination of results. Furthermore, if a participant opts into several studies simultaneously and opts into data sharing, these studies automatically have access to individual-level longitudinal data linked across all studies.

Fluidity in attractions and behaviors among same-sex attracted women has been well-documented, suggesting the appropriateness of dynamical systems modeling of these phenomena over time. As dynamical systems modeling offer an approach to explaining the patterns of complex phenomena, it may be apt for explaining variability in female same-sex sexuality. The present research is the first application of this analytical approach to such data. Dynamical systems modeling, and specifically generalized local linear approximation modeling, was used to fit daily diary data on same-sex attractions and behaviors over a 21 day period among a group of 33 sexual minority women characterized as lesbian, bisexual or "fluid" based on their identity histories. Daily measures of women's reported same-sex attractions were fit using a linear oscillator model and its parameters estimated the cyclicity in these attractions. Results supported the existence of a "core sexual orientation" for women in this sample, regardless of how they identified and despite a high degree of variability in daily same-sex attractions. Thus, modeling individual differences in the variability of attractions and behaviors of sexual minority women may be critical to furthering our understanding of female same-sex sexuality and human sexual orientation more broadly.

In the present paper we investigate weekly fluctuations in the working memory capacity (WMC) assessed over a period of 2 years. We use dynamical system analysis, specifically a second order linear differential equation, to model weekly variability in WMC in a sample of 112 9th graders. In our longitudinal data we use a B-spline imputation method to deal with missing data. The results show a significant negative frequency parameter in the data, indicating a cyclical pattern in weekly memory updating performance across time. We use a multilevel modeling approach to capture individual differences in model parameters and find that a higher initial performance level and a slower improvement at the MU task is associated with a slower frequency of oscillation. Additionally, we conduct a simulation study examining the analysis procedure's performance using different numbers of B-spline knots and values of time delay embedding dimensions. Results show that the number of knots in the B-spline imputation influence accuracy more than the number of embedding dimensions.

A classical twin study was used to estimate the magnitude of genetic and environmental influences on four measurements of within-person variability: dominance flux, warmth flux, spin and pulse. Flux refers to the variability of an individual's interpersonal dominance and warmth. Spin measures changes in the tone of interpersonal styles and pulse measures changes in the intensity of interpersonal styles. Daily reports of interpersonal styles were collected from 494 same-sex female twins (142 monozygotic pairs and 105 dizygotic pairs) over 45 days. For dominance flux, warmth flux, and spin, genetic effects accounted for a larger proportion of variance (37%, 24%, and 30%, respectively) than shared environmental effects (14%, 13%, 0%, respectively), with the remaining variance due to the non-shared environment (62%, 50%, 70% respectively). Pulse appeared to be primarily influenced by the non-shared environment, although conclusions about the contribution of familial influences were difficult to draw from this study.

Personality traits are known to be associated with a host of important life outcomes, including interpersonal dysfunction. The interpersonal circumplex offers a comprehensive system for articulating the kinds of interpersonal problems associated with personality traits. In the current study, traits as measured by the Multidimensional Personality Questionnaire in a sample of 124 young women were correlated with interpersonal dysfunction as measured by the Inventory of Interpersonal Problems-Circumplex. Results suggest that Multidimensional Personality Questionnaire traits vary in their associations with interpersonal distress and in their coverage of specific kinds of interpersonal difficulties among women undergoing the transition to adulthood.

This is an application of contemporary multilevel regression modeling to the prediction of academic performances of 1st-year college students. At a first level of analysis, the data come from N > 16,000 students who were college freshman in 1994-1995 and who were also participants in high-level college athletics. At a second level of analysis, the student data were related to the different characteristics of the C = 267 colleges in Division I of the NCAA. The analyses presented here initially focus on the prediction of freshman GPA from a variety of high school academic variables. The models used are standard multilevel regression models, but we examine nonlinear prediction within these multilevel models, and additional outcome variables are considered. The multilevel results show that (a) high school grades are the best available predictors of freshman college grades, (b) the ACT and SAT test scores are the next best predictors available, (c) the number of high school core units taken does not add to this prediction but does predict credits attained, (d) college graduation rate has a second-level effect of a small negative outcome on the average grades, and (e) nonlinear models indicate stronger effects for students at higher levels of the academic variables. These results show that standard multilevel models are practically useful for standard validation studies. Some difficulties were found with more advanced uses and interpretations of these techniques, and these problems lead to suggestions for further research.

Positive (PA) and negative affect (NA) are important constructs in health and well-being research. Good longitudinal measurement is crucial to conducting meaningful research on relationships between affect, health, and well-being across the lifespan. One common affect measure, the PANAS, has been evaluated thoroughly with factor analysis, but not with Racsh-based latent trait models (RLTMs) such as the Partial Credit Model (PCM), and not longitudinally. Current longitudinal RLTMs can computationally handle few occasions of data. The present study compares four methods of anchoring PCMs across 56 occasions to longitudinally evaluate the psychometric properties of the PANAS plus additional items. Anchoring item parameters on mean parameter values across occasions produced more desirable results than using no anchor, using first occasion parameters as anchors, or allowing anchor values to vary across occasions. Results indicated problems with NA items, including poor category utilization, gaps in the item distribution, and a lack of easy-to-endorse items. PA items had much more desirable psychometric qualities.

The present study investigated reciprocal relationships between adolescent mothers and their children's well-being through an analysis of the coupling relationship of mothers' depressive symptomatology and children's internalizing and externalizing behaviors. Unlike studies using discrete time analyses, the present study used dynamical systems to model time continuously, which allowed for the study of dynamic, transactional effects between members of each dyad. Findings provided evidence of coupling between maternal depressive symptoms and children's behaviors. The most robust finding was that as maternal depressive symptoms became more or less severe, children's behavior problems increased or decreased in a reciprocal manner. Results from this study extended upon theoretical contributions of such authors as Richters (1997) and Granic and Hollenstein (2003), providing empirical validation from a longitudinal study for understanding the ongoing, dynamic relationships between at-risk mothers and their children.

During conversation, women tend to nod their heads more frequently and more vigorously than men. An individual speaking with a woman tends to nod his or her head more than when speaking with a man. Is this due to social expectation or due to coupled motion dynamics between the speakers? We present a novel methodology that allows us to randomly assign apparent identity during free conversation in a video-conference, thereby dissociating apparent sex from motion dynamics. The method uses motion-tracked synthesized avatars that are accepted by naive participants as being live video. We find that 1) motion dynamics affect head movements but that apparent sex does not; 2) judgments of sex are driven almost entirely by appearance; and 3) ratings of masculinity and femininity rely on a combination of both appearance and dynamics. Together, these findings are consistent with the hypothesis of separate perceptual streams for appearance and biological motion. In addition, our results are consistent with a view that head movements in conversation form a low level perception and action system that can operate independently from top-down social expectations.

Resilience is often considered both a trait and a process. The current study proposes a new way to conceptualize resilience-as-process based on dynamical systems modeling, which allows researchers to capture the process of stress management in real time. Coupled damped linear oscillator models succinctly describe daily stress and negative affect in terms of developmental forces (e.g., velocity, acceleration). Models were fit to 56-day daily response data from 42 aging adults (M(age) = 78.8 years; SD(age) = 6.6 years) to observe and understand linkages between daily stress and affect. It was speculated that individuals with greater resilience would experience stress as less coupled to changes in negative affect (less stress reactivity), and would recover their affective equilibrium more quickly following a given exogenous stressor (greater stress recovery). To identify resilience resources related to reliable interindividual differences in coupling and damping between stress and negative affect, we examined possible protective factors. Aspects of personality and social support predicted both the strength and nature of this coupling, such that higher levels of these resources resulted in greater protection from the cost to negative affect from stress, as observed in damping of negative affect and decreased coupling between systems., ((c) 2010 APA, all rights reserved.)

This paper investigates the precision of parameters estimated from local samples of time dependent functions. We find that time delay embedding, i.e. structuring data prior to analysis by constructing a data matrix of overlapping samples, increases the precision of parameter estimates and in turn statistical power compared to standard independent rows of panel data. We show that the reason for this effect is that the sign of estimation bias depends on the position of a misplaced data point if there is no a priori knowledge about initial conditions of the time dependent function. Hence, we reason that the advantage of time delayed embedding is likely to hold true for a wide variety of functions. We support these conclusions both by mathematical analysis and two simulations.

When people speak with one another, they tend to adapt their head movements and facial expressions in response to each others' head movements and facial expressions. We present an experiment in which confederates' head movements and facial expressions were motion tracked during videoconference conversations, an avatar face was reconstructed in real time, and naive participants spoke with the avatar face. No naive participant guessed that the computer generated face was not video. Confederates' facial expressions, vocal inflections and head movements were attenuated at 1 min intervals in a fully crossed experimental design. Attenuated head movements led to increased head nods and lateral head turns, and attenuated facial expressions led to increased head nodding in both naive participants and confederates. Together, these results are consistent with a hypothesis that the dynamics of head movements in dyadicconversation include a shared equilibrium. Although both conversational partners were blind to the manipulation, when apparent head movement of one conversant was attenuated, both partners responded by increasing the velocity of their head movements.

This article addresses three issues germane to experimental design and statistical analysis of intraindividual variability such as the articles contained within this special section. First, the time scale of the measurement of a process can have profound effects on the outcome of analyses of the resulting time series. Measurement in time poses special problems in the design of experiments: the time scale of the measurements must be appropriate for the time scale of the process. Second, deterministic and stochastic models should be fit at the individual level and only at a second level should individual differences in parameters be modeled. Third, one must consider the possibility that nomothetic relations may be exposed by the invariance of covariance between latent variables rather than within a factor analytic measurement model., (PsycINFO Database Record Copyright (c) 2009 APA, all rights reserved)