Psychological Wellbeing,Dyadic Adjustment,and Parental Stress among Spanish Families during the COVID-19 Outbreak

Journal of Child and Family Studies - Due to COVID-19, many countries, like Spain, imposed lockdown on the entire population to prevent the virus from spreading. The purpose of this exploratory...     

Modeling Nonstationary Emotion Dynamics in Dyads using a Time-Varying Vector-Autoregressive Model

Emotion dynamics are likely to arise in an interpersonal context. Standard methods to study emotions in interpersonal interaction are limited because stationarity is assumed. This means that the dynamics, for example, time-lagged relations, are invariant across time periods. However, this is generally an unrealistic assumption. Whether caused by an external (e.g., divorce) or an internal (e.g., rumination) event, emotion dynamics are prone to change. The semi-parametric time-varying vector-autoregressive (TV-VAR) model is based on well-studied generalized additive models, implemented in the software R. The TV-VAR can explicitly model changes in temporal dependency without pre-existing knowledge about the nature of change. A simulation study is presented, showing that the TV-VAR model is superior to the standard time-invariant VAR model when the dynamics change over time. The TV-VAR model is applied to empirical data on daily feelings of positive affect (PA) from a single couple. Our analyses indicate reliable changes in the male’s emotion dynamics over time, but not in the female’s—which were not predicted by her own affect or that of her partner. This application illustrates the usefulness of using a TV-VAR model to detect changes in the dynamics in a system.     

The structure and process of emotional experience following nonmarital relationship dissolution: dynamic factor analyses of love, anger, and sadness

Dynamic factor analysis was used to examine the structure and process of daily emotions in a sample of young adults following a romantic breakup. Participants completed a daily diary for 4 weeks reporting on their love/longing for their ex-partner, anger, and sadness. Using a lag-1 process factor analysis model, results revealed that love/longing, sadness, and anger could be reliably distinguished as separate but correlated mood states in a trivariate model. Four emotional dynamics (amplification, reversing, persistence, and cooccurrence) were operationalized and investigated. Differences in these dynamics were observed on the basis of overall adjustment to the separation and attachment styles. Findings are discussed in terms of attachment and contemporary emotion theories, as well as the need to operationalize time-based affective processes.     

Frequency of stuttering during challenging and supportive virtual reality job interviews

This paper seeks to demonstrate the possibility of manipulating the frequency of stuttering using virtual reality environments (VREs). If stuttering manifests itself in VREs similarly to the way it manifests itself in real world interactions, then VREs can provide a controlled, safe, and confidential method for treatment practice and generalization. Though many researchers and clinicians recognize the need for generalization activities in the treatment of stuttering, achieving generalization in a clinical setting poses challenges to client confidentiality, safety, and the efficient use of a professionals’ time. Virtual reality (VR) technology may allow professionals the opportunity to enhance and assess treatment generalization while protecting the safety and confidentiality of their clients. In this study, we developed a VR job interview environment which allowed experimental control over communication style and gender of interviewers. In this first trial, persons who stutter (PWS) experienced both challenging and supportive VR job interview conditions. The percentage of stuttered syllables was calculated for both interviews for each participant. Self-reported ratings of communication apprehension and confidence were also obtained, and were not significantly correlated with stuttering severity. Results indicated that interviewer communication style affected the amount of stuttering produced by participants, with more stuttering observed during challenging virtual interviews. Additionally, the amount of stuttering observed during the VR job interviews was significantly, positively correlated with the amount of stuttering observed during an interview with the investigator prior to VR exposure. Participants’ subjective reports of the VR experience indicate reactions similar to those they report experiencing in the real world. Possible implications for the use of VR in the assessment and treatment of stuttering are discussed.

Educational objectives: After reading this article, the reader will be able to—(1) list some of the challenges to treatment generalization; (2) describe how virtual reality technology can assist in alleviating some of these challenges; (3) describe how the frequency of stuttering varies across two different virtual environments.     

Latent Differential Equation Modeling of Self-Regulatory and Coregulatory Affective Processes

We examine emotion self-regulation and coregulation in romantic couples using daily self-reports of positive and negative affect. We fit these data using a damped linear oscillator model specified as a latent differential equation to investigate affect dynamics at the individual level and coupled influences for the 2 partners in each couple. Results indicate an absence of damping of relationship-specific affect within individuals in the sample. When both positive and negative affect are modeled at the individual level, the influence of positive affect is greater than that of negative affect. At the dyad level, the findings indicate coupled influences in both positive and negative affect between partners. With regard to positive affect, females are sensitive to their partners' overall displacement from average as well as their rate of change; males are sensitive only to their partners' displacement from average. For negative affect both partners are sensitive to each other's displacement from average, yet there are no coupled influences for rates of change in this dimension. We interpret the influence of the parameters on the system by examining the expected behavior of the system as a function of varying parameter values.     

An Unscented Kalman Filter Approach to the Estimation of Nonlinear Dynamical Systems Models

In the past several decades, methodologies used to estimate nonlinear relationships among latent variables have been developed almost exclusively to fit cross-sectional models. We present a relatively new estimation approach, the unscented Kalman filter (UKF), and illustrate its potential as a tool for fitting nonlinear dynamic models in two ways: (1) as a building block for approximating the log–likelihood of nonlinear state–space models and (2) to fit time-varying dynamic models wherein parameters are represented and estimated online as other latent variables. Furthermore, the substantive utility of the UKF is demonstrated using simulated examples of (1) the classical predator-prey model with time series and multiple–subject data, (2) the chaotic Lorenz system and (3) an empirical example of dyadic interaction.     

Revisiting the Effects of Anger on Risk‐Taking: Empirical and Meta‐Analytic Evidence for Differences Between Males and Females

That anger elicited in one situation can carry over to drive risky behavior in another situation has been described since the days of Aristotle. The present studies examine the mechanisms through which and the conditions under which such behavior occurs. Across three experiments, as well as a meta‐analytic synthesis of the data, results reveal that incidental anger is significantly more likely to drive risky decision making among males than among females. Moreover, the experiments document that, under certain circumstances, such risk‐taking pays off financially. Indeed, the present experiments demonstrate that, because the expected‐value‐maximizing strategy in these studies rewarded risk‐taking, angry‐male individuals earned more money than did both neutral‐emotion males and angry females. In sum, these studies found evidence for robust disparities between males and females for anger‐driven risk‐taking. Importantly, although men did not experience more anger than women, they did show a heightened tendency to respond to anger with risk‐taking. Published 2016. This article has been contributed to by US Government employees and their work is in the public domain in the USA.     

Usability and Utility of a Computerized Cognitive-Behavioral Self-Help Program for Public Speaking Anxiety

This study describes the use of a cognitive-behavioral computer-administered self-help program with minimal therapist contact for public speaking anxiety. Participants (N = 10) with social phobia, as measured by a structured clinical interview, completed the self-help program in an open clinical trial. The program was delivered via a CD-ROM during individual sessions at an outpatient clinic, including 4 sessions of psychoeducation/cognitive therapy and 4 sessions of exposure therapy using a virtual audience presented on a computer screen. A therapist was available in another room to answer questions and to debrief for up to 10 minutes after each session. Participants completed standardized self-report questionnaires assessing social anxiety at pretreatment, posttreatment, and 3-month follow-up. Participants also completed measures of computer usability. Results showed decreases on all self-report measures of social anxiety from pre- to posttreatment, which were maintained at follow-up (n = 8). Participants also reported that they were satisfied with treatment, that they felt improved after treatment, and that the computer program was user-friendly. This study provides preliminary evidence that a computer-administered cognitive-behavioral-based program that includes minimal therapist contact may reduce public speaking anxiety and suggests that further research with a controlled design is needed.     

Cross-Sectional Analysis of Longitudinal Mediation Processes

Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.