We present a mixed-effects location scale model (MELSM) for examining the daily dynamics of affect in dyads. The MELSM includes person and time-varying variables to predict the location, or individual means, and the scale, or within-person variances. It also incorporates a submodel to account for between-person variances. The dyadic specification can accommodate individual and partner effects in both the location and the scale components, and allows random effects for all location and scale parameters. All covariances among the random effects, within and across the location and the scale are also estimated. These covariances offer new insights into the interplay of individual mean structures, intra-individual variability, and the influence of partner effects on such factors. To illustrate the model, we use data from 274 couples who provided daily ratings on their positive and negative emotions toward their relationship - up to 90 consecutive days. The model is fit using Hamiltonian Monte Carlo methods, and includes subsets of predictors in order to demonstrate the flexibility of this approach. We conclude with a discussion on the usefulness and the limitations of the MELSM for dyadic research.

We examine the daily exchanges in affect and emotional experiences of individuals in dyads using a mixed-effects location scale model. We argue that this method is useful to characterize the daily fluctuations in emotions for each individual as well as their interrelations over time. Furthermore, we illustrate how to consider the potential effect of factors external to the dyads' emotion dynamics, an aspect often ignored in emotion research. In particular, we show how daily weather may influence within-person variability of affect toward one's relationship, beyond the influence of one's and the partner's affect. We interpret our findings in the context of emotion research and methodology for dyadic interactions.

We present a mixed-effects location scale model (MELSM) for examining the daily dynamics of affect in dyads. The MELSM includes person and time-varying variables to predict the location, or individual means, and the scale, or within-person variances. It also incorporates a submodel to account for between-person variances. The dyadic specification can accommodate individual and partner effects in both the location and the scale components, and allows random effects for all location and scale parameters. All covariances among the random effects, within and across the location and the scale are also estimated. These covariances offer new insights into the interplay of individual mean structures, intra-individual variability, and the influence of partner effects on such factors. To illustrate the model, we use data from 274 couples who provided daily ratings on their positive and negative emotions toward their relationship - up to 90 consecutive days. The model is fit using Hamiltonian Monte Carlo methods, and includes subsets of predictors in order to demonstrate the flexibility of this approach. We conclude with a discussion on the usefulness and the limitations of the MELSM for dyadic research.

Romantic relationships are defined by emotion dynamics, or how the emotions of one partner at a single timepoint can affect their own emotions and the emotions of their partner at the next timepoint. Previous research has shown that the level of these emotion dynamics plays a role in determining the state and quality of the relationship. However, this research has not examined whether the estimated emotion dynamics change over time, and how the change in these dynamics might relate to relationship outcomes, despite changes in dynamics being likely to occur. We examined whether the magnitude of variation in emotion dynamics over time was associated with relationship outcomes in a sample of 148 couples. Time-varying vector autoregressive models were used to estimate the emotion dynamics for each couple, and the average and standard deviation of the dynamics over time was related to relationship quality and relationship dissolution 1-2 years later. Our results demonstrate that certain autoregressive and cross-lagged parameters do show significant variation over time, and that this variation is associated with relationship outcomes. Overall, this study demonstrates the importance of accounting for change in emotion dynamics over time, and the relevance of this change to the prediction of future outcomes.

Fluid reasoning is the cornerstone of human cognition, both during development and in adulthood. Despite this, the neural mechanisms underlying the development of fluid reasoning are largely unknown. In this review, we provide an overview of this important cognitive ability, the method of measurement, its changes over the childhood and adolescence of an individual, and its underlying neurobiological underpinnings. We review important findings from psychometric, cognitive, and neuroscientific literatures, and outline important future directions for this interdisciplinary research.

Youth who exhibit externalizing problems during childhood and adolescence are at an increased risk for a wide range of detrimental life outcomes. Despite the profound consequences of externalizing problems for children, their families, and their communities, we know less about the precise trajectory of externalizing symptoms across late childhood and adolescence, because of the paucity of fine-grained longitudinal research. The present study examined the development of externalizing symptoms in a large sample (N = 674) of Mexican-origin youth, assessed annually from age 10 to 17. Specifically, we conducted analyses to better understand the trajectories of attention-deficit-hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) symptoms (and their codevelopment), as well as how gender and cultural factors influence symptom trajectories. On average, ADHD symptoms slowly declined from age 10 to 17; ODD symptoms increased until age 13 and then declined thereafter; and, CD symptoms slowly increased until age 15 and then leveled off. ADHD, ODD, and CD symptoms predicted change in each other, indicating youth may accumulate multiple forms of externalizing problems over time. Boys reported fewer externalizing problems than girls, contrary to expectations. Consistent with the Immigrant Paradox, we found that 2nd + generation youth, youth who endorsed fewer traditional Mexican cultural values (traditional gender roles, traditional family values, and religiosity), and youth who engaged in less Spanish/more English language use were at increased risk for exhibiting ADHD, ODD, and CD symptoms from childhood through adolescence. We discuss the theoretical and practical implications of these developmental patterns among Mexican-origin youth. (PsycINFO Database Record

The goal of this fMRI study was to examine how well developmental improvements in reasoning ability can be explained by changes in functional connectivity between specific nodes in prefrontal and parietal cortices. To this end, we examined connectivity within the lateral fronto-parietal network (LFPN) and its relation to reasoning ability in 132 children and adolescents aged 6-18 years, 56 of whom were scanned twice over the course of 1.5 years. Developmental changes in strength of connections within the LFPN were most prominent in late childhood and early adolescence. Reasoning ability was related to functional connectivity between left rostrolateral prefrontal cortex (RLPFC) and inferior parietal lobule (IPL), but only among 12-18-year olds. For 9-11-year olds, reasoning ability was most strongly related to connectivity between left and right RLPFC; this relationship was mediated by working memory. For 6-8-year olds, significant relationships between connectivity and performance were not observed; in this group, processing speed was the primary mediator of improvement in reasoning ability. We conclude that different connections best support reasoning at different points in development and that RLPFC-IPL connectivity becomes an important predictor of reasoning during adolescence.

Continuous-time (CT) models are a flexible approach for modeling longitudinal data of psychological constructs. When using CT models, a researcher can assume one underlying continuous function for the phenomenon of interest. In principle, these models overcome some limitations of discrete-time (DT) models and allow researchers to compare findings across measures collected using different time intervals, such as daily, weekly, or monthly intervals. Theoretically, the parameters for equivalent models can be rescaled into a common time interval that allows for comparisons across individuals and studies, irrespective of the time interval used for sampling. In this study, we carry out a Monte Carlo simulation to examine the capability of CT autoregressive (CT-AR) models to recover the true dynamics of a process when the sampling interval is different from the time scale of the true generating process. We use two generating time intervals (daily or weekly) with varying strengths of the AR parameter and assess its recovery when sampled at different intervals (daily, weekly, or monthly). Our findings indicate that sampling at a faster time interval than the generating dynamics can mostly recover the generating AR effects. Sampling at a slower time interval requires stronger generating AR effects for satisfactory recovery, otherwise the estimation results show high bias and poor coverage. Based on our findings, we recommend researchers use sampling intervals guided by theory about the variable under study, and whenever possible, sample as frequently as possible. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

In this article we present an exploratory tool for extracting systematic patterns from multivariate data. The technique, hierarchical segmentation (HS), can be used to group multivariate time series into segments with similar discrete-state recurrence patterns and it is not restricted by the stationarity assumption. We use a simulation study to describe the steps and properties of HS. We then use empirical data on daily affect from one couple to illustrate the use of HS for describing the affective dynamics of the dyad. First, we partition the data into three periods that represent different affective states and show different dynamics between both individuals’ affect. We then examine the synchrony between both individuals’ affective states and identify different patterns of coherence across the periods. Finally, we discuss the possibilities of using results from HS to construct confirmatory dynamic models with multiple change points or regime-specific dynamics.