Dehumanization of women and men in elective abortion: A preregistered replication in China

Although abortion in China has been legalized for several decades, it is still controversial and the woman and man involved are at risk of being stigmatized. The current research replicated two studies conducted in Italy by investigating how the abortion decision of the woman and man influence received moral outrage, dehumanization, and perceptions of female professional competence. Study 1, which only included female participants, found higher moral outrage toward a woman having an abortion when the pregnancy was depicted as referring to a “child” (vs. “foetus”), and higher dehumanization of the woman in terms of human nature. Study 2, which included both male and female participants, and clarified responsibility for the abortion decision with respect to the woman and the man, revealed higher moral outrage and a reduction in the attribution of human uniqueness to the man, and of human nature to both the woman and the man. Moral outrage mediated dehumanization when participants had low positive attitudes toward abortion in Study 1 and high positive attitudes in Study 2. Both studies showed a negative impact of the decision to have an abortion on the woman's perceived traditional professional competence. The results generally align with the findings from Italy and yield some practical implications on reducing abortion stigma. Future abortion stigma research should consider more gender-related factors.     

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.