Intraindividual time-varying dynamic network of affects: linear autoregressive mixed-effects models for ecological momentary assessment

An interesting recent development in emotion research and clinical psychology is the discovery that affective states can be modeled as a network of temporally interacting moods or emotions. Additionally, external factors like stressors or treatments can influence the mood network by amplifying or dampening the activation of specific moods. Researchers have turned to multilevel autoregressive models to fit these affective networks using intensive longitudinal data gathered through ecological momentary assessment. Nonetheless, a more comprehensive examination of the performance of such models is warranted. In our study, we focus on simple directed intraindividual networks consisting of two interconnected mood nodes that mutually enhance or dampen each other. We also introduce a node representing external factors that affect both mood nodes unidirectionally. Importantly, we disregard the potential effects of a current mood/emotion on the perception of external factors. We then formalize the mathematical representation of such networks by exogenous linear autoregressive mixed-effects models. In this representation, the autoregressive coefficients signify the interactions between moods, while external factors are incorporated as exogenous covariates. We let the autoregressive and exogenous coefficients in the model have fixed and random components. Depending on the analysis, this leads to networks with variable structures over reasonable time units, such as days or weeks, which are captured by the variability of random effects. Furthermore, the fixed-effects parameters encapsulate a subject-specific network structure. Leveraging the well-established theoretical and computational foundation of linear mixed-effects models, we transform the autoregressive formulation to a classical one and utilize the existing methods and tools. To validate our approach, we perform simulations assuming our model as the true data-generating process. By manipulating a predefined set of parameters, we investigate the reliability and feasibility of our approach across varying numbers of observations, levels of noise intensity, compliance rates, and scalability to higher dimensions. Our findings underscore the challenges associated with estimating individualized parameters in the context of common longitudinal designs, where the required number of observations may often be unattainable. Moreover, our study highlights the sensitivity of autoregressive mixed-effect models to noise levels and the difficulty of scaling due to the substantial number of parameters.

Daily Levels and Dynamic Metrics of Affective-Cognitive Constructs Associate With Suicidal Thoughts and Behaviours in Adults After Psychiatric Hospitalization

The period after psychiatric hospitalization is an extraordinarily high-risk period for suicidal thoughts and behaviours (STBs). Affective-cognitive constructs (ACCs) are salient risk factors for STBs, and intensive longitudinal metrics of these constructs may improve personalized risk detection and intervention. However, limited research has examined how within-person daily levels and between-person dynamic metrics of ACCs relate to STBs after hospital discharge. Adult psychiatric inpatients (N = 95) completed a 65-day ecological momentary assessment protocol after discharge as part of a 6-month follow-up period. Using dynamic structural equation models, we examined both within-person daily levels and between-person dynamic metrics (intensity, variability and inertia) of positive and negative affect, rumination, distress intolerance and emotion dysregulation as risk factors for STBs. Within-person lower daily levels of positive affect and higher daily levels of negative affect, rumination, distress intolerance and emotion dysregulation were risk factors for next-day suicidal ideation (SI). Same-day within-person higher rumination and negative affect were also risk factors for same-day SI. At the between-person level, higher overall positive affect was protective against active SI and suicidal behaviour over the 6-month follow-up, while greater variability of rumination and distress intolerance increased risk for active SI, suicidal behaviour and suicide attempt. The present study provides the most comprehensive examination to date of intensive longitudinal metrics of ACCs as risk factors for STBs. Results support the continued use of intensive longitudinal methods to improve STB risk detection. Interventions focusing on rumination and distress intolerance may specifically help to prevent suicidal crises during critical transitions in care.

Outlining a novel psychometric model of mental flexibility and affect dynamics

Theoretically, affective states have always been conceived as complex phenomena enabling individuals to respond flexibly and dynamically to environmental demands. Methodologically, the novel field of Affect Dynamics has started to analyze affective states as inherently dynamic and interdependent phenomena by focusing on how and why they fluctuate over time. Fluctuations of affective states can also be conceived as a function of individuals' ability to flexibly modulate their responses according to environmental demands. However, this ability has been sparsely investigated in different disciplines and domains, thus, engendering a plethora of terms and models. In this conceptual analysis, we first aimed to disentangle the puzzle of flexibility by outlining the distinctive cross-domain features of this concept, thus providing a novel comprehensive operationalization. We termed this novel unitary concept "mental flexibility," the general ability to variably adapt to environmental demands. Then, we outlined the interplay between individuals' mental flexibility and affect dynamics by proposing a novel psychometric model of affect dynamics, using Markovian chain.

Do markers of daily affect mediate associations between interpretation bias and depressive symptoms? A longitudinal study of early adolescents

INTRODUCTION

Early adolescence represents a time of heightened vulnerability for depression. Negative interpretation biases have been associated with increases in depressive symptoms during this developmental period; however, the mechanisms underlying the association between interpretation biases and depression remain poorly understood. Cognitive theories posit that interpretation biases give rise to depression by modulating daily affect, particularly in the context of stress. However, this has not yet been directly examined. The present study tested affect intensity and instability as mechanisms linking negative interpretation biases with change in adolescent depressive symptoms.

METHODS

Ninety-four adolescents (aged 11-13 years; 51% boys) from Vancouver, Canada, were recruited for this longitudinal study. At baseline (Time 1), participants self-reported depressive symptoms and completed the Scrambled Sentences Task to assess negative interpretation biases. Next, participants completed daily diaries to assess positive affect (PA) and negative affect (NA) during a naturalistic stressor-the first 2 weeks of high school (Time 2). Finally, participants self-reported depressive symptoms 3 months later (Time 3). Path models were conducted to test whether PA and NA intensity and instability mediated prospective associations between negative interpretation biases and depressive symptom changes.

RESULTS

Although NA intensity, NA instability, and PA instability predicted increases in depressive symptoms, only NA intensity mediated associations between interpretation biases and symptom changes. Neither PA intensity nor instability mediated these associations.

CONCLUSIONS

Elevated daily NA represents a specific mechanism through which stronger negative interpretation biases predict increases in depressive symptoms in adolescence.

The emotional component of insomnia disorder: A focus on emotion regulation and affect dynamics in relation to sleep quality and insomnia

Theoretical models of insomnia disorder recognise an emotional component in the maintenance of the disorder. Nonetheless, the field of emotions is vast and different processes are involved in psychological well-being. The present narrative review focusses on emotion regulation and affect dynamics, synthesising some of the most recent and relevant evidence on emotions in relation to the quality of sleep and to insomnia disorder. The literature underlines the close association between impaired sleep quality and difficulties in regulating emotions. Impaired sleep quality is also associated with reduced positive affect and increased negative affect, but little evidence supports a bi-directional association between affective states and sleep. Affect variability in relation to sleep has been less investigated. Initial evidence suggests that high variability in positive affect has a negative impact on sleep. Neurobiological and behavioural evidence indicates that insomnia disorder is associated with emotion dysregulation, negative affect, and a distinct daily profile of affective states. More research is needed on the affective experience of patients with insomnia disorder, adopting multiple sampling of affect across the day and the week. Understanding how the unfolding of emotions over time interact with sleep alterations may help to improve the tailoring and monitoring of treatments addressing disturbed emotional processes in insomnia disorder.

Affect variability and physical health: The moderating role of mean affect

Research has only begun to explore how affect variability relates to physical health and has typically not assessed long-term associations nor considered the moderating role of mean affect. Therefore, we used data from the Midlife in the United States Study waves 2 (N = 1512) and 3 (N = 1499) to test how affect variability predicted concurrent and long-term physical health while also testing the moderating role of mean affect. Results indicated that greater negative affect variability was associated concurrently with a greater number of chronic conditions (p = .03) and longitudinally with worse self-rated physical health (p < .01). Greater positive affect variability was associated concurrently with more chronic conditions (p < .01) and medications (p < .01) and longitudinally with worse self-rated physical health (p = .04). Further, mean negative affect played a moderating role such that at lower levels of mean negative affect, as affect variability increased, so did the number of concurrent chronic conditions (p < .01) and medications (p = .03) and the likelihood of reporting worse long-term self-rated physical health (p < .01). Thus, the role of mean affect should be considered when testing short- and long-term associations between affect variability and physical health.

Exploring Biomarkers of Mental Flexibility in Healthy Aging: A Computational Psychometric Study

Mental flexibility (MF) has long been defined as cognitive flexibility. Specifically, it has been mainly studied within the executive functions domain. However, there has recently been increased attention towards its affective and physiological aspects. As a result, MF has been described as an ecological and cross-subject skill consisting of responding variably and flexibly to environmental cognitive-affective demands. Cross-sectional studies have mainly focused on samples composed of healthy individual and of patients with chronic conditions such as Mild Cognitive Impairment and Parkinson's, emphasizing their behavioral rigidity. Our study is the first to consider a sample of healthy older subjects and to outline physiological and psychological markers typical of mental flexibility, to identify functional biomarkers associated with successful aging. Our results reveal that biomarkers (respiratory and heart rate variability assessments) distinguished between individuals high vs. low in mental flexibility more reliably than traditional neuropsychological tests. This unveiled the multifaceted nature of mental flexibility composed of both cognitive and affective aspects, which emerged only if non-linear multi-variate analytic approaches, such as Supervised Machine Learning, were used.

Overnight emotional inertia in relation to depressive symptomatology and subjective sleep quality

Emotions show a certain degree of continuity during the day, a quality referred to as emotional inertia, and that is typically elevated in depression. Little is known however about the extent to which our emotional experiences may or may not also persist overnight. Do our feelings continue from evening to morning or not? And how is this related to depressive symptoms and sleep quality? In an experience sampling studies in healthy subjects (ns = 123) we investigated (1) to what extent people's mood, in terms of positive and negative affect, in the morning, after a night of sleep, can be predicted from their mood of the evening before, and whether this is moderated by (2) depressive symptom severity or (3) subjective sleep quality. Results showed that morning negative affect could be strongly predicted based on previous evening negative affect, whilst this carry-over effect was not observed for positive affect, indicating that negative affect shows a general tendency to persist overnight, while positive affect did not show such continuity. The overnight prediction of both negative and positive affect was not moderated by level of depressive symptoms, nor by subjective sleep quality.

Music Lessons for the Study of Affect

Some accounts of the evolution of music suggest that it emerged from emotionally expressive vocalizations and serves as a necessary counterweight to the cognitive elaboration of language. Thus, emotional expression appears to be intrinsic to the creation and perception of music, and music ought to serve as a model for affect itself. Because music exists as patterns of changes in sound over time, affect should also be seen in patterns of changing feelings. Psychologists have given relatively little attention to these patterns. Results from statistical approaches to the analysis of affect dynamics have so far been modest. Two of the most significant treatments of temporal patterns in affect-sentics and vitality affects have remained outside mainstream emotion research. Analysis of musical structure suggests three phenomena relevant to the temporal form of emotion: affect contours, volitional affects, and affect transitions. I discuss some implications for research on affect and for exploring the evolutionary origins of music and emotions.

Using the Ball-in-Bowl Metaphor to Outline an Integrative Framework for Understanding Dysregulated Emotion

Dysregulated emotion plays an important role for mental health problems. To elucidate the underlying mechanisms, researchers have focused on the domains of strategy-based emotion regulation, psychophysiological self-regulation, emotion evaluations, and resulting emotion dynamics. So far, these four domains have been looked at in relative isolation from each other, and their reciprocal influences and interactive effects have seldom been considered. This domain-specific focus constrains the progress the field is able to make. Here, we aim to pave the way towards more cross-domain, integrative research focused on understanding the raised reciprocal influences and interactive effects of strategy-based emotion-regulation, psychophysiological self-regulation, emotion evaluations, and emotion dynamics. To this aim, we first summarize for each of these domains the most influential theoretical models, the research questions they have stimulated, and their strengths and weaknesses for research and clinical practice. We then introduce the metaphor of a ball in a bowl that we use as a basis for outlining an integrative framework of dysregulated emotion. We illustrate how such a framework can inspire new research on the reciprocal influences and interactions between the different domains of dysregulated emotion and how it can help to theoretically explain a broader array of findings, such as the high levels of negative affect in clinical populations that have not been fully accounted for by deficits in strategy-based emotion regulation and the positive long-term consequences of accepting and tolerating emotions. Finally, we show how it can facilitate individualized emotion regulation interventions that are tailored to the specific regulatory impairments of the individual patient.

Negative Emotion Differentiation Predicts Psychotherapy Outcome: Preliminary Findings

Emotion differentiation (ED), the extent to which same-valenced emotions are experienced as distinct, is considered a valuable ability in various contexts owing to the essential affect-related information it provides. This information can help individuals understand and regulate their emotional and motivational states. In this study, we sought to examine the extent to which ED can be beneficial in psychotherapy context and specifically for predicting treatment response. Thirty-two prospective patients with mood and anxiety disorders completed four daily assessments of negative and positive emotions for 30 days before receiving cognitive-behavioral treatment. Depression, stress, and anxiety symptoms severity were assessed pre- and post-treatment using self-reports and clinical interviews. We conducted a series of hierarchical regression models in which symptoms change scores were predicted by ED while adjusting for the mean and variability. We found that negative ED was associated with greater self-reported treatment response (except for anxiety) when negative emotional variability (EV) was included in the models. Probing negative ED and EV's interactive effects suggested that negative ED was associated with greater treatment response (except for anxiety) for individuals with lower EV levels. Results were obtained while controlling for mean negative affect. Our findings suggest that negative ED can benefit psychotherapy patients whose negative emotions are relatively less variable. We discuss the meaning of suppression and interactive effects between affect dynamics and consider possible clinical implications.

Synchrony with distress in affective empathy and compassion

Sensitivity to suffering of others is a core factor in social cohesion and evolutionary success. The emergence of such sensitivity may occur via two neuro-functional mechanisms. One is sharing the pain and distress of others, which relies on affective empathy. The other involves a caring concern for others' wellbeing, termed compassion. Both affective empathy and compassion are triggered by cues of pain and distress, exhibited by suffering targets. Yet, the mechanisms underlying distress processing in empathy and compassion are not clear. In the current research, we investigated synchrony with a target's distress, as a putative mechanism for continuous processing of distress cues. Participants viewed a video of a target in distress when given two different instructions: they were asked to continuously rate their distress in the affective empathy condition, or their feelings of care in the compassion condition. We used these dynamic ratings as well as participants' autonomic and facial responses to assess multi-channel synchrony with the target's self-rated distress fluctuations. Dynamic ratings and facial corrugator responses were significantly positively synchronized with the target's distress. For the corrugator responses, synchrony with the target was more pronounced than synchrony with participants' own ratings. Autonomic responses exhibited negative synchrony with the target's distress. Synchrony was higher in the affective empathy than in the compassion condition, across channels. These results point to the key role of subjective and physiological synchrony with the target's distress in empathic sharing of negative experiences. They also highlight the attenuation of embodied resonance with distress in compassionate experiences.

Using free association networks to extract characteristic patterns of affect dynamics

The dynamics of human affect in day-to-day life are an intrinsic part of human behaviour. Yet, it is difficult to observe and objectively measure how affect evolves over time with sufficient resolution. Here, we suggest an approach that combines free association networks with affect mapping, to gain insight into basic patterns of affect dynamics. This approach exploits the established connection in the literature between association networks and behaviour. Using extant rich data, we find consistent patterns of the dynamics of the valence and arousal dimensions of affect. First, we find that the individuals represented by the data tend to feel a constant pull towards an affect-neutral global equilibrium point in the valence-arousal space. The farther the affect is from that point, the stronger the pull. We find that the drift of affect exhibits high inertia, i.e. is slow-changing, but with occasional discontinuous jumps of valence. We further find that, under certain conditions, another metastable equilibrium point emerges on the network, but one which represents a much more negative and agitated state of affect. Finally, we demonstrate how the affect-coded association network can be used to identify useful or harmful trajectories of associative thoughts that otherwise are hard to extract.

Emotional Inertia is Associated with Lower Well-Being when Controlling for Differences in Emotional Context

Previous studies have linked higher emotional inertia (i.e., a stronger autoregressive slope of emotions) with lower well-being. We aimed to replicate these findings, while extending upon previous research by addressing a number of unresolved issues and controlling for potential confounds. Specifically, we report results from two studies (Ns = 100 and 202) examining how emotional inertia, assessed in response to a standardized sequence of emotional stimuli in the lab, correlates with several measures of well-being. The current studies build on previous research by examining how inertia of both positive emotions (PE) and negative emotions (NE) relates to positive (e.g., life satisfaction) and negative (e.g., depressive symptoms) indicators of well-being, while controlling for between-person differences in the mean level and variability of emotions. Our findings replicated previous research and further revealed that (a) NE inertia was more strongly associated with lower well-being than PE inertia; (b) emotional inertia correlated more consistently with negative indicators (e.g., depressive symptoms) than positive indicators (e.g., life satisfaction) of well-being; and (c) these relationships were independent of individual differences in mean level and variability of emotions. We conclude, in line with recent findings, that higher emotional inertia, particularly of NE, may be an indicator of increased vulnerability to depression.