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.     

The nature of residual cost in regular switch response factors

Two experiments are presented that compare the residual cost found when switching from one task to another under predictable conditions. The aim of the study was to explore the roles played by the stimulus, the response, or both in the process of the mental set reconfiguration necessary to switch between two tasks. The experiments tested [Rogers, R. D., & Monsell, S. (1995). Cost of a predictable switch between simple cognitive tasks. Journal of Experimental Psychology: General, 124, 207-231] stimulus-cued-completion hypothesis and [Schuch, S., & Koch, I. (2003). The role of response selection for inhibition of task sets in task shifting. Journal of Experimental Psychology: Human Perception and Performance, 29, 92-105] hypothesis of response selection as the key factor in the nature of switch cost. In the first experiment, two conditions were created that varied in terms of a Go/No-Go signal: The Go trials were a replication of [Tornay, F. J., & Milán, E. G. (2001). A more complete task-set reconfiguration in random than in predictable task switch. The Quarterly Journal of Experimental Psychology, 54A, 785-803 Experiment 3]; The No-Go trials were identical to the first condition, except that participants did not execute a response in the trial n-1 (Schuch & Koch, 2003). In addition, the percentage of Go and No-Go trials was manipulated. The results showed that the cost was significant only in the high Go signal-frequency case (Experiment 2), with an abrupt offset in Go trials and a gradual offset in No-Go trials. Based on the results of these experiments, it was concluded that the crucial factor to complete a mental set reconfiguration is response-related and not stimulus-related.     

Why professional athletes need a prolonged period of warm-up and other peculiarities of human motor learning

Professional athletes involved in sports that require the execution of fine motor skills must practice for a considerable length of time before competing in an event. Why is such practice necessary? Is it merely to warm-up the muscles, tendons, and ligaments, or does the athlete's sensorimotor network need to be constantly recalibrated? In this article, the authors present a point of view in which the human sensorimotor system is characterized by: (a) a high noise level and (b) a high learning rate at the synaptic level (which, because of the noise, does not equate to a high learning rate at the behavioral level). They argue that many heuristics of human skill learning, including the need for a prolonged period of warm-up in experts, follow from these assumptions.     

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.