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Facial emotion expressions play a central role in interpersonal interactions; these displays are used to predict and influence the behavior of others. Despite their importance, quantifying and analyzing the dynamics of brief facial emotion expressions remains an understudied methodological challenge. Here, we present a method that leverages machine learning and network modeling to assess the dynamics of facial expressions. Using video recordings of clinical interviews, we demonstrate the utility of this approach in a sample of 96 people diagnosed with psychotic disorders and 116 never-psychotic adults. Participants diagnosed with schizophrenia tended to move from neutral expressions to uncommon expressions (e.g., fear, surprise), whereas participants diagnosed with other psychoses (e.g., mood disorders with psychosis) moved toward expressions of sadness. This method has broad applications to the study of normal and altered expressions of emotion and can be integrated with telemedicine to improve psychiatric assessment and treatment.

Background: Psychological well-being is becoming an increasingly important part of the public health mission, and is multifaceted and has various interrelated dimensions. A thorough understanding of its presentation patterns (i.e., subtypes) and change process is essential for effective interventions. However, longitudinal research examining the subtypes of psychological well-being among Chinese older adults remains scarce, and little is known about the factors influencing the belonging and transition of these subtypes. Methods: We used two waves of national longitudinal data (2011 and 2014) from the Chinese Longitudinal Healthy Longevity Survey, with 5778 respondents aged 65 and above. Psychological well-being was assessed by eight indicators from three dimensions: quality of life, positive feelings, and negative feelings. Latent profile transition analysis was used to identify subtypes of psychological well-being and explore the transition of older adults among different subtypes over time. Results: Four distinct subtypes of psychological well-being were identified, with the Mainly Unhappy Subtype being prevalent among Chinese older adults. Unhappiness and uselessness were indicative; eliminating unhappiness was vital to complete psychological recovery; feeling of uselessness was found as a crossroads in the process of psychological health. The psychological well-being of older adults was unstable; emotion blunting was found in the transition from the Mainly Unhappy Subtype to the Satisfied and Positive Subtype, and emotion augmentation was found in the transition from the Resilient yet Useless Subtype to the Discontented and Negative Subtype. A paradox in aging was observed in the membership of the Mainly Unhappy Subtype and the Discontented and Negative Subtype. Marital status, residence, education, household income, exercise, and leisure activity were significantly positively related to both initial membership and transition of the psychological well-being subtypes. Smoking status, drinking status, and social support were significantly associated with the transition between subtypes. Physical health was significantly correlated with the initial subtype membership. Conclusions: Our findings suggest some indicators and critical steps in improving psychological well-being among older adults. These provide insight for healthcare professionals and policymakers to develop tailored interventions considering subtypes and individual characteristics. [ABSTRACT FROM AUTHOR]

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.

Emotion-related impulsivity—the engagement in impulsive reactions specifically in response to emotions—is considered a transdiagnostic factor underlying psychopathology. The reflexive responding to emotion (RRE) model of emotion-related impulsivity (Carver et al., 2008) suggests that sensitivities to reward and threat in combination with control over emotion are factors that result in internalizing and externalizing psychopathology. In the current study, we adapt the trait-based RRE model to momentary states by evaluating how within-person fluctuations in affect combine with perceptions of momentary emotional control to predict impulsive, rash action and inaction in daily life. Participants (college students and adults recruited from the community: N = 197) completed 8 days of ecological momentary assessment, where we assessed current affect, perceptions of momentary emotional control (via distress intolerance and willpower), and urges for rash action and inaction (5,353 momentary prompts completed). We also assessed subsequent engagement in rash action and inaction. Using multilevel modeling, we found that when people feel greater positive affect and lower negative affect, they also report greater subjective willpower and lower distress intolerance, replicating past ecological momentary assessment findings. Furthermore, we found that momentary perceptions of momentary emotional control moderated the relationship between (a) affect and urges for rash action and (b) affect and engagement in rash action at follow-up. Findings support a dynamic model of the RRE model, confirming that perceptions of momentary emotional control are relevant for both rash action and inaction, particularly when occurring alongside shifts in affect. General Scientific Summary: The tendency to respond impulsively to emotion is associated with a broad range of psychopathology. To better understand when and for whom emotion-related impulsivity is problematic, we investigated how fluctuations in people's ability to manage emotions are linked to rash behavior throughout the day. Our findings highlight how momentary shifts in affect and emotional control play an important role in people's engagement in rash behavior. [ABSTRACT FROM AUTHOR]

Prior research suggests that youth experience immediate emotional distress following privacy invasion, but it is unclear whether and how persistent privacy invasion over longer periods destabilizes the intrapersonal emotional regulatory functions that privacy serves. This study investigated whether late adolescents and emerging adults who reported different patterns of maternal privacy invasion over a full academic year showed differences in emotion regulation, as reflected by the frequency and lability of negative and positive emotional experiences. Participants were first-year university students (n = 349; 60.2% female) in Hong Kong, aged 17 to 24 (MT1 = 18.20, SDT1 = 1.10). They self-reported maternal privacy invasion perceptions and negative and positive emotions 16 times, at bi-weekly intervals. Latent class growth analyses divided participants into a Higher Invasion Perceptions group (24.9%), a Moderate Invasion Perceptions group (46.4%), and a Lower Invasion Perceptions group (28.7%). A one-way MANCOVA examined potential differences in negative and positive emotion dynamics across groups, indexed by frequency (means) and instability (mean square of successive differences and probability of acute change). In line with predictions, both the Higher Invasion and Moderate Invasion groups reported higher negative emotion frequency and instability, compared to the Lower Invasion group. However, groups showed no significant differences regarding positive emotion frequency and instability. The findings indicate that perceptions of maternal privacy invasion predict youth's negative emotion regulation processes over their first collegiate year. Prolonged, higher levels of maternal privacy invasion may disrupt the intrapersonal regulatory functions that privacy serves over time. [ABSTRACT FROM AUTHOR]

Although alcohol use is related to daily affect, findings regarding affect variability—the degree to which individuals exhibit day-to-day fluctuations in affect—and alcohol use have been mixed. The present study assessed whether individuals who use alcohol tend to have higher positive and negative affect variability than individuals who do not, as well as whether higher affect variability is related to more frequent and intense alcohol use among individuals who use alcohol. We also explored whether affect variability differed between individuals who use alcohol and those who concurrently use alcohol and cannabis. College students (N = 1909; M = 20.1, SD = 1.3; 67.7% female; 76.6% white; n = 1147 individuals who use alcohol) completed a 21-day protocol between February-December 2021 in which they reported daily affect, number of drinks consumed, and any cannabis use. As hypothesized, individuals who used alcohol had higher positive and negative affect variability than individuals who did not. Among individuals who used alcohol, individuals with higher negative affect variability drank alcohol more frequently, and both higher positive and negative affect variability were related to more intense alcohol use. No differences emerged between individuals who used alcohol and those who concurrently used alcohol and cannabis. Taken together, higher positive and negative affect variability were related to higher odds of using alcohol and more frequent and intense use among individuals who use alcohol, over and above average affect. Higher affect variability could relate to alcohol use because of difficulties with emotion regulation or heightened sensitivity to the environment. [ABSTRACT FROM AUTHOR]

Objective: Depressive symptom dynamics, including change trajectories and symptom variability, have been related to therapy outcomes. However, such dynamics have often been examined separately and related to outcomes of interest using two-step analyses, which are characterized by several limitations. Here, we show how to overcome these limitations using location-scale models in a dynamic structural equation modeling framework. Method: We introduce location-scale modeling in an accessible manner to pave the way for its use in research integrating within-person dynamics and intervention-related change in psychopathology, and we illustrate this modeling approach in a large-scale internet-based intervention for depression (N = 1,656). Using eight data points sampled across about 8 weeks, we predicted improvement across the intervention (50% symptom reduction) as a function of early change and symptom variability. Results: Early symptom change was associated with a more likely improvement across therapy. Variability of symptoms beyond change trajectories during the intervention was associated with less likely improvement. Conclusions: Location-scale models, and dynamic structural equation modeling more generally, are well suited to modeling how patterns of symptom change during psychotherapy are related to important (e.g., therapy) outcomes. Our illustrative application of location-scale modeling showed that symptom variability was associated with less overall improvement in depressive symptoms. However, this finding requires replication with more intensive sampling of symptoms before final conclusions can be drawn on when and how to distinguish maladaptive from adaptive variability during psychotherapy. What is the public health significance of this article?: During psychotherapy, multiple mechanisms of change should work simultaneously and in concert. As this study highlights, it is important to examine multiple types of symptom dynamics during psychotherapy simultaneously to understand the processes and ultimately the causes of therapeutic change. [ABSTRACT FROM AUTHOR]

• Examined within-person emotion dynamics and their relationship to suicidal ideation. • Anger variability moderated the association between anger intensity and ideation. • Anger intensity with higher variability was positively linked to suicide ideation. • Anger intensity together with its variability are risk states for suicidal ideation. Previous research has shown a relationship between proximal (i.e., close-in-time) emotion experiences and suicidal ideation (SI). Yet, it remains unclear which emotion processes (i.e., the level of the emotion [intensity], how much emotions vary [variability], emotional consistency [inertia], how specific emotions are [differentiation]) and which emotions (i.e., sadness, hopelessness, anger, nervousness, happiness) are most potent predictors of SI. Seventy-seven adolescents (67.5% assigned female at birth) completed daily diaries for 4 weeks after psychiatric hospitalization. Levels of the above-mentioned emotions and frequency of SI were recorded. For each week and each emotion, mean (intensity), standard deviation (variability), autocorrelation (inertia), and intraclass correlation coefficients (ICCs; negative emotion differentiation) were calculated (i.e., four observations/person). Multilevel models examined whether (a) mean intensity, variability, and their interaction; and (b) mean intensity, inertia, and their interaction, were related to mean weekly SI frequency. A separate model examined whether negative emotion differentiation was related to mean weekly SI frequency after adjusting for mean intensity. A significant interaction between mean intensity of anger and variability of anger emerged (B = 0.54, SE = 0.24, p =.023); a positive relationship between mean anger and mean SI frequency was present at moderate or high levels of anger variability but not at its low levels. Mean intensity of most emotions was related to SI frequency in the expected directions. No other statistically significant findings emerged. Results revealed the importance of considering multiple emotion features, their dynamic nature, and their combined effect. Future research should explore mechanisms accounting for anger being related to heightened proximal SI, along with an examination of effective intervention strategies to reduce anger intensity and variability. [ABSTRACT FROM AUTHOR]

Emotions are highly dynamic and social in nature. Traditional approaches to studying emotion expression face obstacles such as substantial time investments, susceptibility to human biases, and limited capacity to capture nuanced emotional patterns. To address these challenges, this research leveraged text mining and sentiment analysis to explore the dynamic patterns of emotion expression within the context of mother‐child interactions. We analyzed 8,841 conversation transcripts involving 1,462 mother‐child dyads, sourced from the Child Language Data Exchange System. Polarity scores were calculated and analyzed to uncover the temporal patterns of mother and child emotional sentiment. Our findings revealed that mothers tended to exhibit heightened levels of positive emotion at the beginning and conclusion of conversations, whereas children displayed a more linear positive trend. Using model‐based cluster analysis, we identified two distinct clusters of mothers characterized by varying degrees of emotion expression variation and two clusters of children showing different rates of elevation in positive emotion. At the dyadic level, the differences between mother and child polarity scores varied as a function of time, with an increase of difference from the beginning to the 20th percentile point, a decrease until the 90th percentile, and then an increase again towards the end of the conversation. This study demonstrates the utility of text mining and sentiment analysis in developmental studies, particularly in the context of parent‐child interactions. The findings hold informative implications for interventions that focus on fostering healthy parent‐child relationships. [ABSTRACT FROM AUTHOR]

Objective: Interpersonal and emotional functioning are closely linked and reciprocally influence one another. Contemporary integrative interpersonal theory (CIIT) offers a useful framework to conceptualize these patterns and guide interventions in cases where these patterns result in dysfunction. Stress processes offer several dynamic frameworks to guide empirical investigations using methods that allow for fine-grained analyses in the context of daily life. Method: Four samples of adults (Sample 1, N = 145; Sample 2, N = 160; Sample 3, N = 297; Sample 4 = 89 dyads, 178 individuals) completed ecological momentary assessment protocols focused on a variety of interpersonal and emotional experiences. Samples were enriched for aggressive and self-harming behavior (Sample 1), trait hostility (Sample 2), interpersonal problems (Sample 3), and personality disorder features (Sample 4). Results: Using multilevel dynamic structural equation modeling, we investigated how emotions and interpersonal functioning operate over brief timescales in daily life. We found evidence for a vicious socioemotional cycle across all four samples, whereby negative emotions related to interpersonal conflict (i.e., perceptions of and enacting cold, antagonistic, or quarrelsome behavior; components that contribute to the interpersonal situation from the perspective of CIIT) which in turn related to increased negative emotions. Although individuals differed in the strength of this process, it was unrelated to trait negative affectivity. Conclusions: Viewing these results through the lens of CIIT, we discuss multiple intervention points highlighted by these dynamic results whereby the vicious cycle might be changed. What is the public health significance of this article?: Emotions have an essential role in how we navigate interpersonal situations. As a result, emotions can maintain and exacerbate problems in interpersonal functioning. This study highlights specific ways emotions may lead to interpersonal problems and offers several points where clinicians may intervene on these problems. [ABSTRACT FROM AUTHOR]

Background: Dominant psychotherapies target how individuals experience and understand their daily emotion. Therefore, research examining how daily emotions influence long-term mental health outcomes may help inform treatment development. Methods: This investigation applied a multi-cohort (n = 378; n = 460), longitudinal design to test how reports of daily emotion predict psychological symptoms, loneliness, and wellbeing one-year later. Dynamic indices (polarity, inertia) reflecting "how" emotional experiences are conceptualized moment-to-moment and static indices (person-mean, standard deviation) of emotion were extracted from 10 daily reports. Each index was modelled individually, in concert with others, and in relation to a key dispositional factor in symptom development: trait anxiety. Results: Dynamic indices predicted outcomes one-year later, but only the effect of positive emotional inertia remained significant after accounting for mean intensity of affect. Daily reports of emotion also predicted small but significant variance in outcomes beyond trait anxiety. Conclusions: Results highlight the role of daily subjective experiences of emotion in long-term mental health outcomes and reinforce their importance as targets for treatment. [ABSTRACT FROM AUTHOR]

Background: Individuals in a depressive episode and healthy controls exhibit robust differences on affect dynamics captured with ecological momentary assessment (EMA). However, few studies have explored affect dynamics in individuals in remission from depression, and results have been mixed. Methods: A community sample of 18-year-olds (N = 345) completed diagnostic interviews and EMA probing emotions and low interest/motivation 5× daily for 2 weeks. Affect home base, variability, and inertia were compared across currently depressed, remitted, and never-depressed groups. Results: Both depression groups had a higher negative affect (NA) and low interest/motivation home base, lower positive affect (PA) home base, greater variability of NA, PA, and low interest/motivation, and greater NA and low interest/motivation inertia than never-depressed participants. Additionally, the currently depressed group had a higher sad home base specifically, greater variability across most negative emotions and low interest/motivation, and greater low interest/motivation inertia than the remitted group. The currently depressed and remitted groups did not differ in anxious, upset, or PA home base, anxious or PA variability, and inertia of all negative emotions and PA. Conclusions: Findings suggest that a number of abnormalities in emotion and reward functioning persist after a depressive episode resolves, however, the tendency to experience higher levels of sadness, greater range of a variety of negative emotions, and more variable and persistent low interest/motivation are exacerbated during depressive episodes. Conversely, greater intensity and persistence of some negative emotions (anxiety, upset) and blunted positive emotions appear to equally characterize depression in both the symptomatic and remitted state. [ABSTRACT FROM AUTHOR]

The practices described in Buddhist philosophy are essentially a suite of non-theistic cognitive and behavioral interventions designed to induce nonattachment (N-A), which can be defined in terms of the absence of a need for one's personal reality to be other than it is. Although meditative practices have received attention in multiple literatures, the cognitive analogs to these behaviorally-oriented practices have not. Two experiments involving undergraduate participants (total N = 239; M age = 19.04) investigated whether the provision of wisdom related to the Three Marks of Existence (i.e., some degree of suffering is inevitable, there is impermanence, and many events are not in our control) could result in (1) higher nonattachment attitudes, (2) lower threat appraisals, (3) lower stressor reactivity, and (4) shorter emotion reaction durations. With moderate to large effect sizes, the Three Marks trainings (relative to placebo or control conditions) resulted in (1) higher nonattachment attitudes, (2) lower threat appraisals, (3) no differences in negative emotional intensity, but 4) shorter emotion durations. These results provide preliminary evidence that enduring cognitive trainings such as the Three Marks can be an effective tool to increase acceptance-related attitudes while attenuating negative reactivity. [ABSTRACT FROM AUTHOR]

Abstract Background In intimate relationships, which are characterized by emotional interdependence, partners act as attachment figures which serve emotion regulation functions. The experience of emotions as well as the strategies that partners use to regulate them and to respond to relational experiences, especially during stressful periods, differ greatly according to their attachment orientation. An important aspect in emotion dynamics is emotional inertia, which reflects the degree to which a person’s current affective state is resistant to change on a moment-to-moment basis. Inertia has been related to maladaptive emotion regulation strategies, like suppression and rumination, preferentially used by highly anxious and avoidant individuals. The aim of this study is to examine associations between attachment orientations and reports on the experience of positive and negative affect, and their dynamics in daily life across the transition to parenthood. Methods Longitudinal data from a sample of 152 mixed-gender couples collected across the transition to parenthood was analyzed. We predicted that individuals with a more insecure attachment would report more negative and less positive affect, and that their emotional experience would be more resistant to change over time. We explored effects when participants reported feeling stressed. Results The data suggested that attachment anxiety was associated with less positive and more negative affect and that attachment avoidance was associated with more positive affect. Anxious individuals showed lower emotional inertia and not higher as we expected. Reported stress for anxious and avoidant individuals was significantly associated with more negative but not less positive affect. Conclusions Results are discussed in the light of their impact on couples during stressful periods. Differences between anxiety and avoidance are found, emphasizing the importance of attachment insecurities on the experience of emotion. Furthermore, our findings on momentary fluctuating affect offer complementary insight into the emotional functioning of individuals with different attachment orientations.

The present study aimed at (1) identifying the emotion regulation processes that can explain the trait hedonism positive emotions relationship, and (2) determining whether this relationship varies according to which part of the distribution of positive emotions is considered. To this end, the intensity of the positive emotions felt by 84 adult participants, and the intensity of their engagement in savoring, were assessed twice a day over a month period, using an Experience Sampling Method. Participants' distributions of these variables were then related to their level of trait hedonism, assessed with a common questionnaire. Results suggested that, as expected, the trait hedonism positive emotions relationship varied according to which part of the distribution of positive emotions was considered. In their worst times (i.e., first centiles of each individual's distribution), individuals with low versus high trait hedonism differed very little from each other. By contrast, in more favorable times (i.e., following centiles), individuals with high levels of trait hedonism experienced more intense positive emotions than individuals with low levels of trait hedonism. This phenomenon was mediated by individuals' engagement in savoring. These results are discussed in light of current theories on emotion dynamics. [ABSTRACT FROM AUTHOR]

Introduction: Mood and anxiety disorders are characterized by abnormal levels of positive affect (PA), negative affect (NA) and changes in how emotions unfold over time. To better prevent and treat those disorders, it is crucial to determine which kind of indices of emotion dynamics best predict elevated depressive and generalized anxiety symptoms. Methods: 221 individuals (60 men; mean age = 46 years, SD = 15 years) completed a 7-day ecological momentary assessment study, where their positive and negative affective experience was assessed 5 times a day. For each participant, the intensity, instability, inertia, and differentiation of PA and NA were calculated. The Estonian Emotional State Questionnaire was used to assess depressive and generalized anxiety disorder (GAD) symptoms. Results: We found that NA and PA intensity, and NA instability predicted elevated depressive and GAD symptoms. Models including NA instability alongside PA and NA intensity showed the best fit for both depression and generalized anxiety, as NA instability alongside other variables significantly increased the odds of having elevated depressive and GAD symptoms. Affective inertia, differentiation, and PA instability were not associated with depressive and GAD symptoms. Discussion: In addition to the mean levels of affect, it is important to study other emotion dynamic indices such as NA instability, as these offer a more nuanced view of underlying emotion dysregulation processes. This could, in the longterm, help tailor more specific prevention and intervention methods for mood and anxiety disorders. [ABSTRACT FROM AUTHOR]

Background: In intimate relationships, which are characterized by emotional interdependence, partners act as attachment figures which serve emotion regulation functions. The experience of emotions as well as the strategies that partners use to regulate them and to respond to relational experiences, especially during stressful periods, differ greatly according to their attachment orientation. An important aspect in emotion dynamics is emotional inertia, which reflects the degree to which a person's current affective state is resistant to change on a moment-to-moment basis. Inertia has been related to maladaptive emotion regulation strategies, like suppression and rumination, preferentially used by highly anxious and avoidant individuals. The aim of this study is to examine associations between attachment orientations and reports on the experience of positive and negative affect, and their dynamics in daily life across the transition to parenthood. Methods: Longitudinal data from a sample of 152 mixed-gender couples collected across the transition to parenthood was analyzed. We predicted that individuals with a more insecure attachment would report more negative and less positive affect, and that their emotional experience would be more resistant to change over time. We explored effects when participants reported feeling stressed. Results: The data suggested that attachment anxiety was associated with less positive and more negative affect and that attachment avoidance was associated with more positive affect. Anxious individuals showed lower emotional inertia and not higher as we expected. Reported stress for anxious and avoidant individuals was significantly associated with more negative but not less positive affect. Conclusions: Results are discussed in the light of their impact on couples during stressful periods. Differences between anxiety and avoidance are found, emphasizing the importance of attachment insecurities on the experience of emotion. Furthermore, our findings on momentary fluctuating affect offer complementary insight into the emotional functioning of individuals with different attachment orientations. [ABSTRACT FROM AUTHOR]

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 autoregressivemodels 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. [ABSTRACT FROM AUTHOR]

Introduction & Background An estimated 4.95 billion people used social media in 2023, with the average user active on around seven platforms for over two hours per day. This widespread use leads to abundant digital footprint data around interactions with social media. These data can be collected continuously and reflect real behaviour of users in naturalistic settings. These strengths have led researchers to propose the use of social media data in digital phenotyping, where digital footprints can be used to quantify and predict health conditions. Mental health assessment in particular could benefit, as existing approaches, such as self-report questionnaires and inpatient assessment, are unable to perform the real-time monitoring that digital phenotyping could potentially achieve. Digital phenotyping models for mental health require careful consideration of what aspects of social media data to include. Including all data users generate could result in models that are overfitted and difficult to explain. Studies are required that explore the relationship between specific aspects of social media data, such as the time course of expressed emotion, and gold-standard measures of mental health. Objectives & Approach With participants’ consent, we linked Twitter data to self-reported measures of mental health from the Avon Longitudinal Study of Parents and Children. We performed sentiment analysis using three different approaches—LIWC, VADER and RoBERTa—to estimate the amount, variability and instability of positive and negative emotional content in each participant’s Tweets over a one-year period. We explored the association between these measures of emotion expression and self-reported scores of depressive symptoms, anxiety symptoms and wellbeing. These mental health measures are the Short Mood and Feelings Questionnaire, the Generalized Anxiety 7 and the Warwick Edinburgh Mental Wellbeing Scale. Relevance to Digital Footprints Our research is highly relevant to digital footprint research, as it involves the use of digital footprint data (i.e. Twitter data) to predict mental health outcomes. Conclusions & Implications The results of our analysis will inform the development of digital footprint based phenotyping for mental health that could one day provide information to supplement clinical assessments.

IntroductionMood and anxiety disorders are characterized by abnormal levels of positive affect (PA), negative affect (NA) and changes in how emotions unfold over time. To better prevent and treat those disorders, it is crucial to determine which kind of indices of emotion dynamics best predict elevated depressive and generalized anxiety symptoms.Methods221 individuals (60 men; mean age = 46 years, SD = 15 years) completed a 7-day ecological momentary assessment study, where their positive and negative affective experience was assessed 5 times a day. For each participant, the intensity, instability, inertia, and differentiation of PA and NA were calculated. The Estonian Emotional State Questionnaire was used to assess depressive and generalized anxiety disorder (GAD) symptoms.ResultsWe found that NA and PA intensity, and NA instability predicted elevated depressive and GAD symptoms. Models including NA instability alongside PA and NA intensity showed the best fit for both depression and generalized anxiety, as NA instability alongside other variables significantly increased the odds of having elevated depressive and GAD symptoms. Affective inertia, differentiation, and PA instability were not associated with depressive and GAD symptoms.DiscussionIn addition to the mean levels of affect, it is important to study other emotion dynamic indices such as NA instability, as these offer a more nuanced view of underlying emotion dysregulation processes. This could, in the long-term, help tailor more specific prevention and intervention methods for mood and anxiety disorders.

With the rising popularity of intensive longitudinal research, the modeling techniques for such data are increasingly focused on individual differences. Here we present mixture multilevel vector-autoregressive modeling, which extends multilevel vector-autoregressive modeling by including a mixture, to identify individuals with similar traits and dynamic processes. This exploratory model identifies mixture components, where each component refers to individuals with similarities in means (expressing traits), autoregressions, and cross-regressions (expressing dynamics), while allowing for some interindividual differences in these attributes. Key issues in modeling are discussed, where the issue of centering predictors is examined in a small simulation study. The proposed model is validated in a simulation study and used to analyze the affective data from the COGITO study. These data consist of samples for two different age groups of over 100 individuals each whowere measured for about 100 days. We demonstrate the advantage of exploratory identifying mixture components by analyzing these heterogeneous samples jointly. The model identifies three distinct components, and we provide an interpretation for each component motivated by developmental psychology. In psychological research, individuals are sometimes assessed repeatedly on certain attributes, for example indicating their mood, for an extended period of time. Researchers are then interested to describe the between-individual differences that are present in the resulting intensive longitudinal data. In order to describe these differences, we propose the novel mixture multilevel vector-autoregressive model. This model identifies subgroups of individuals with similar traits and dynamic processes, while also allowing for between-individual differences in these attributes within the different subgroups. We illustrate the model in an application to emotion dynamics data. The sample consists of individuals from two different age groups. Using our mixture multilevel vector-autoregressive model, we describe the between-individual differences that are present in the emotion dynamics of this sample. We do so by describing the more homogeneous subgroups that were identified by our model, and by describing the between-individual differences within the different subgroups. [ABSTRACT FROM AUTHOR]

Social media plays an important role in disaster risk reduction and management. This study brings text, emotion, and timeline analyses together to increase our understanding of online travel community members' emotional dynamics and meaningful themes of concerns during the early months of the COVID-19 pandemic. Qualitative data was collected from three online travel communities on Reddit. We found that, as the pandemic continued, Redditors' concerns shifted from context-related external issues to daily-life-related internal issues. Furthermore, group-based emotions formed by the virtual community evolved positively, which can be beneficial for restoring confidence in travel after the pandemic. After presenting the results, we discuss their theoretical and practical implications. [ABSTRACT FROM AUTHOR]

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.

Psychology has witnessed a dramatic increase in the use of intensive longitudinal data (ILD) to study within-person processes, accompanied by a growing number of indices used to capture individual differences in within-person dynamics (WPD). The reliability of WPD indices is rarely investigated and reported in empirical studies. Unreliability in these indices can bias parameter estimates and yield erroneous conclusions. We propose an approach to (a) estimate the reliability and (b) correct for sampling error of WPD indices using "Level-1 variance-known" (V-known) multilevel models (Raudenbush & Bryk, 2002). When WPD indices are calculated for each individual, the sampling variance of the observed WPD scores is typically falsely assumed to be zero. V-known models replace this "zero" with an approximate sampling variance fixed at Level 1 to estimate the true variance of the index at Level 2, following random effects meta-analysis principles. We demonstrate how V-known models can be applied to a broad range of emotion dynamics commonly derived from ILD, including indices of the average level (mean), variability (intraindividual standard deviation), instability (probability of acute change), bipolarity (correlation), differentiation (intraclass correlation), inertia (autocorrelation), and relative variability (relative standard deviation) of emotions. A simulation study shows the usefulness of V-known models to recover the true reliability of these indices. Using a 21-day diary study, we illustrate the implementation of the proposed approach to obtain reliability estimates and to correct for unreliability of WPD indices in real data. The techniques may facilitate psychometrically sound inferences from WPD indices in this burgeoning research area. [ABSTRACT FROM AUTHOR]

Parent-adolescent emotion dynamics are central to psychosocial adjustment during this developmental period. Perspective taking-the ability to take another's point of view into consideration-develops significantly during adolescence and is important for successful interpersonal functioning in contexts such as conflicts between family members. We used grid-sequence analysis (Brinberg, Fosco, & Ram, 2017) to examine interdyad differences in mother-adolescent emotion dynamics during a conflict discussion, and whether interdyad differences were associated with maternal and adolescent perspective taking. Mothers and their typically developing adolescents (N = 49, Mage = 14.84 years) were video-recorded during a 10-min conflict discussion. We identified patterns of multistep chains of expressed emotions that unfolded during the conflict and how interdyad differences in those patterns were associated with maternal and adolescent perspective taking. Dyads differed with respect to whether they showed turn taking in validation and interest behaviors, or whether they showed patterns of reciprocated negative affect. Higher adolescent but not maternal perspective taking was associated with dyadic turn taking of validation and interest. Maternal and adolescent perspective taking were not associated with the pattern of reciprocated negative affect. Taken together, results highlight the importance of examining the complex process of emotion dynamics in parent-adolescent interactions. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

There is growing interest in affective computing for the representation and prediction of emotions along ordinal scales. However, the term ordinal emotion label has been used to refer to both absolute notions such as low or high arousal, as well as relation notions such as arousal is higher at one instance compared to another. In this paper, we introduce the terminology absolute and relative ordinal labels to make this distinction clear and investigate both with a view to integrate them and exploit their complementary nature. We propose a Markovian framework referred to as Dynamic Ordinal Markov Model (DOMM) that makes use of both absolute and relative ordinal information, to improve speech based ordinal emotion prediction. Finally, the proposed framework is validated on two speech corpora commonly used in affective computing, the RECOLA and the IEMOCAP databases, across a range of system configurations. The results consistently indicate that integrating relative ordinal information improves absolute ordinal emotion prediction. [ABSTRACT FROM AUTHOR]

Emotional dysregulation is thought to underlie risk for both anxiety and depressive disorders. However, despite high rates of comorbidity, anxiety and depression are phenotypically different. Apart from nosological differences (e.g., worry for anxiety, low mood for depression), it remains unclear how the emotional dysregulation inherent in individual differences in trait anxiety and depression severity present on a day-to-day basis. One approach that may facilitate addressing these questions is to utilize Ecological Momentary Assessment (EMA) using mobile phones to parse the temporal dynamics of affective experiences into specific parameters. An emerging literature in affective science suggests that risk for anxiety and depressive disorders may be associated with variation in the mean and instability/variability of emotion. Here we examine the extent to which distinct temporal dynamic parameters uniquely predict risk for anxiety versus depression. Over 10 days, 105 individuals rated their current positive and negative affective state several times each day. Using two distinct approaches to statistically assess mean and instability of positive and negative affect, we found that individual differences in trait anxiety was generally associated with increased instability of positive and negative affect whereas mean levels of positive and negative affect were generally associated with individual differences in depression. These data provide evidence that the emotional dysregulation underlying risk for mood versus anxiety disorders unfolds in distinct ways and highlights the utility in examining affective dynamics to understand psychopathology. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

In today’s world, there are many people suffering from mental health problems such as depression and anxiety. If these conditions are not identified and treated early, they can get worse quickly and have far-reaching negative effects. Unfortunately, many people suffering from these conditions, especially depression and hypertension, are unaware of their existence until the conditions become chronic. Thus, this paper proposes a novel approach using Bi-directional Long Short-Term Memory (Bi-LSTM) algorithm and Global Vector (Glo Ve) algorithm for the prediction and treatment of these conditions. Smartwatches and fitness bands can be equipped with these algorithms which can share data with a variety of IoT devices and smart systems to better understand and analyze the user’s condition. We compared the accuracy and loss of the training dataset and the validation dataset of the two models namely, Bi-LSTM without a global vector layer and with a global vector layer. It was observed that the model of Bi-LSTM without a global vector layer had an accuracy of 83%,while Bi-LSTM with a global vector layer had an accuracy of 86% with a precision of 86.4%, and an F1 score of 0.861. In addition to providing basic therapies for the treatment of identified cases, our model also helps prevent the deterioration of associated conditions, making our method a real-world solution. [ABSTRACT FROM AUTHOR]

Daily emotion dynamics provide valuable information about individuals' emotion processes as they go about their lives. Emotion dynamics such as emotion levels (mean), emotion variability (degree of fluctuation), and emotion network density (strength of temporal connections among emotions) are associated with risks for various psychopathology in youth and adults. Prior work has shown that caregivers and friends play crucial socializing roles in adolescent emotional well‐being, but less is known about their roles in daily emotion dynamics. This study examined whether caregiver emotion coaching, caregiver‐adolescent closeness, and friendship quality were associated with adolescents' emotion levels, emotion variability, and emotion network density. Further, we examined whether caregiver‐adolescent closeness moderated the associations between coaching and emotion dynamics. Participants were 150 adolescents (61% girls; Mage = 14.75) and one of their caregivers (95% female; Mage = 43.35) who completed a baseline survey and 21 daily surveys. Results showed that caregiver emotion coaching interacted with caregiver‐adolescent closeness in predicting emotion levels and variability. Specifically, when closeness was higher, emotion coaching was significantly associated with lower sadness and anger levels, higher happiness levels, and lower happiness variability. Caregiver emotion coaching, independent of closeness, was also associated with lower anxiety levels, lower sadness variability, and lower emotion network density. Friendship quality was significantly associated with lower levels of sadness, anxiety, and anger, higher levels of happiness, and lower variability in anxiety and anger. These findings suggest that caregivers and friends are central to everyday emotion levels and variability and a more flexible emotion system in adolescents. [ABSTRACT FROM AUTHOR]

Although there is evidence parent–child attachment security is associated with trait-like emotion indices, trait perspectives do not fully capture children's responses to context, an important emotion regulation component. This paper evaluates whether attachment is associated with two dynamic emotion indicators: emotion reactivity and emotion recovery. We review conceptual and empirical connections, describe the dynamic emotion perspective, discuss hypotheses, and review evidence. Our review (15 studies) shows that secure attachment was more consistently related to recovery than reactivity, avoidant attachment was related to low emotion reactivity and recovery, ambivalent attachment was associated with greater emotion reactivity, and disorganized attachment was related to high reactivity and recovery difficulties. We close by comparing trait-like and dynamic emotion conclusions then propose future research directions. [ABSTRACT FROM AUTHOR]

Fetal adaptations to prenatal maternal stress may confer high risk for childhood behavior problems, potentially operating via dynamic fluctuations in infants' emotions during mother–infant interactions. These fluctuations over time may give rise to behavior problems. Among a sample of 210 low-income mothers of Mexican origin and their 24-week-old infants, dynamic structural equation modeling was used to examine whether within-infant second-by-second emotion processes were predicted by maternal prenatal stress and predicted behavior problems at 36 and 54 months. The mean level around which infant negative affect fluctuated was related to prenatal stress, but not to childhood behavior problems. The volatility in infant negative affect, reflecting greater ebb and flow in infant negative affect during playful interaction, was predicted by prenatal stress and predicted enduring behavior problems in childhood. Results highlight a potential child-driven pathway linking prenatal exposure with childhood behavior problems via infant negative emotional volatility. [ABSTRACT FROM AUTHOR]

More instability (MSSD) and variability (SD) of negative affect (NA) have been related to current and future depressive symptoms. We investigated whether NA instability and variability were predictive of the rate of symptom improvement during treatment and of reaching remission status. Forty-six individuals with major depressive disorder completed six days of ecological momentary assessments (10 beeps/day) before starting a combination of pharmacotherapy and supportive therapy. During and after treatment, the Hamilton Depression Rating Scale (HDRS) diagnostic interview was performed monthly for 18 months. The rate of change in HDRS scores over five months (during treatment) and remission status (HDRS ≤ 7) over eighteen months were predicted by the SD, MSSD and Mean of NA, and relevant baseline predictors. Higher Mean NA and baseline symptom severity, but not NA variability or instability, predicted stronger depressive symptom reduction during treatment. Higher odds of remitting were only associated with lower Mean NA. Higher mean NA may indicate more room for improvement and thus for a steeper rate of symptom change, while lower mean NA levels may imply that remission status is more readily reached. To resolve the inconclusive findings for instability and variability as predictors of symptom improvement replication in larger samples is required. [ABSTRACT FROM AUTHOR]

Affective disturbances in psychosis are well-documented but our understanding of their phenotypic nature in everyday life remains limited. Filling this gap could advance mechanistic models of the affective pathway to psychosis and pave the ground for new research avenues. Therefore, this study focused on temporal affect dynamics in psychosis, i.e., the patterns with which affect fluctuates over time. We applied experience-sampling with nine assessments per day over one week in participants with psychotic disorders (PSY), participants with attenuated psychotic symptoms (AS), clinical controls with obsessive compulsive disorders (OCD) and healthy controls (HC; total N = 139) to assess whether in PSY and AS, dynamics in affective valence and arousal are characterized by higher instability (i.e., extreme and frequent moment-to-moment fluctuations of affect intensities), higher affective variability (i.e., larger range of affect intensities), or lower inertia (i.e., higher frequency of affective changes), compared to the clinical and healthy control samples. Mixed-model ANOVAs revealed higher instability in both PSY and OCD compared to HC, but no significant differences for variability and inertia. AS had an intermediate position and did not differ significantly from any other group. We found evidence for small to medium effect sizes for the influence of mean affect levels on the dynamic indicators. Our findings indicate that individuals with psychotic disorders have increased affective instability and that this could be a transdiagnostic phenomenon. Zooming in on the variability and inertia components did not confer additional benefits. Emotion-focused interventions for psychosis should focus on reducing frequent and extreme affective fluctuations. [ABSTRACT FROM AUTHOR]

Understanding the emotional dimension of classroom practices benefits sustainable education. However, the dynamic nature of teacher emotions during teaching remains understudied. To acquire an "in-depth" description of the emotional phenomena during teaching, a case study was adopted to examine the pattern and dynamics of a senior secondary mathematics teacher's teaching-related emotions. Seven new mathematics lessons were observed and videotaped, and three video-stimulated, post-lesson teacher interviews were conducted. Based on appraisal theories of emotions, thematic analysis and content analysis were mainly used to analyse the data. The study revealed that the teacher most frequently experienced happiness/satisfaction as positive emotions and confusion/surprise as negative emotions. The emotions followed a three-stage process: perceptions, multilevel appraisals, and responses. The teacher most often perceived her students' cognitive accuracy and adopted goal/need conduciveness/attainment, the discrepancy from expectations, and causal attribution as core appraisal dimensions. Teacher emotions in classrooms were also often hierarchical and cumulative, shifting from more negative at the beginning of a new semester to more positive at the middle of the semester. This study shed light on the dynamic development of teacher emotions, extending the theories and process models of emotions from the psychological field to educational research and facilitating teachers' sustainable professional development. [ABSTRACT FROM AUTHOR]

Human interlocutors may use emotions as an important signaling device for coordinating an interaction. In this context, predicting a significant change in a speaker's emotion may be important for regulating the interaction. Given the nonlinear and noisy nature of human conversations and relatively short time series they produce, such a predictive model is an open challenge, both for modeling human behavior and in engineering artificial intelligence systems for predicting change. In this paper, we present simple and theoretically grounded models for predicting the direction of change in emotion during conversation. We tested our approach on textual data from several massive conversations corpora and two different cultures: Chinese (Mandarin) and American (English). The results converge in suggesting that change in emotion may be successfully predicted, even with regard to very short, nonlinear, and noisy interactions. [ABSTRACT FROM AUTHOR]

Dynamic patterns of influence between parents and children have long been considered key to understanding family relationships. Despite this, most observational research on emotion in parent-child interactions examines global behaviors at the expense of exploring moment-to-moment fluctuations in emotions that are important for relational outcomes. Using recurrence quantification analysis (RQA) and growth curve analysis, this investigation explored emotion dynamics during parent-adolescent conflict interactions, focusing not only on concurrently shared emotional states but also on time-lagged synchrony of parents' and adolescents' emotions relative to one another. Mother-adolescent dyads engaged in a 10-min conflict discussion and reported on their satisfaction with the process and outcome of the discussion. Emotions were coded using the Specific Affect Coding System (SPAFF) and were collapsed into the following categories: negativity, positivity, and validation/interest. RQA and growth curve analyses revealed that negative and positive emotions were characterized by a concurrently synchronous pattern across all dyads, with the highest recurrence rates occurring around simultaneity. However, lower levels of concurrent synchrony of negative emotions were associated with higher discussion satisfaction. We also found that patterns of negativity differed with age: Mothers led negativity in dyads with younger adolescents, and adolescents led negativity in dyads with older adolescents. In contrast to negative and positive emotions, validation/interest showed the time-lagged pattern characteristic of turn-taking, and more highly satisfied dyads showed stronger patterns of time-lagged coordination in validation/interest. Our findings underscore the dynamic nature of emotions in parent-adolescent interactions and highlight the important contributions of these moment-to-moment dynamics toward overall interaction quality. (PsycINFO Database Record

Human emotions are dynamic in nature. The intensity with which they are felt changes over time, and they have a natural timescale of expression, from onset to decay. Further, emotions shade from one to another, and many feelings are built up of blends of pure emotions. In order to represent this complex reality visually, a variety of models of the space of emotions have been introduced in the psychology literature. In this paper we present a computational approach to transforming facial expressions of emotions into positions in one of these models, the activation-evaluation space. This enables the study of emotion dynamics from facial expressions, including the transitions between emotion states and changes in emotion intensity through time, based on a set of shape models for different emotions. We consider different ways to build these shape models, and then show how to represent each frame of emotion in the activation-evaluation space, and analyse sequences of emotions from video by following temporal trajectories in that space. We demonstrate the approach on a standard dataset and compare it to human annotations, with promising results. [ABSTRACT FROM AUTHOR]

We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR EC . Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners' cognitive load to keep their emotional experience at optimal levels to enhance learning. [ABSTRACT FROM AUTHOR]

To advance the understanding of the dynamic relationship between brain activities and emotional experiences, we examined the neural patterns of tension, a unique emotion that highly depends on how an event unfolds. Specifically, the present study explored the temporal relationship between functional connectivity patterns within and between different brain functional modules and the fluctuation in tension during film watching. Due to the highly contextualized and time-varying nature of tension, we expected that multiple neural networks would be involved in the dynamic tension experience. Using the neuroimaging data of 546 participants, we conducted a dynamic brain analysis to identify the intra- and inter-module functional connectivity patterns that are significantly correlated with the fluctuation of tension over time. The results showed that the inter-module connectivity of cingulo-opercular network, fronto-parietal network, and default mode network is involved in the dynamic experience of tension. These findings demonstrate a close relationship between brain functional connectivity patterns and emotional dynamics, which supports the importance of functional connectivity dynamics in understanding our cognitive and emotional processes. [ABSTRACT FROM AUTHOR]

Although irritability and sadness are cardinal symptoms of depression, they are also common in preschoolers. The daily experiences of these emotions are not well-understood during early childhood, yet may provide insight into identification of early depressive symptoms. The current longitudinal study examined daily mean levels and emotion dynamics of preschool-aged children's irritability and sadness and psychiatric outcomes in early school-age. Parents (n = 291) completed 14 consecutive daily diaries about their preschoolers' emotions. Two years later, parents (n = 164) completed a semi-structured clinical interview and questionnaires about their children's psychological functioning. Strong correlations between mean and dynamic measures (rs = 0.65–0.91) were identified. Preschoolers' mean daily levels and dynamics of irritability (variability, instability, inertia) and sadness (instability, inertia) predicted symptoms and impairment 2 years later. Sadness instability and inertia continued to predict difficulties after adjusting for mean daily sadness. Fine-grained daily measures of preschoolers' affect may be help identify children at-risk for psychological problems. [ABSTRACT FROM AUTHOR]

Emotional inertia is a central feature of emotion dynamics and it refers to the degree to which emotional states are self‐predictive and linger over time, describing their resistance to change. It is related to several indicators of maladjustment, yet there is limited research on its correlates in the workplace, where it may be particularly relevant as emotional experiences deeply influence organizational life. In two experience‐sampling studies, we studied temporal dependency of negative emotional states at work in terms of emotional inertia. In a first study (n = 128), we investigated the association between exhaustion, the core dimension of burnout, with inertia of negative emotions. In a second study (n = 116), we aimed to replicate findings from the first study and additionally examined the moderating role of inertia of negative emotions in the relationship between negative emotions at work and counterproductive work behaviour. The findings show that exhaustion is consistently associated with inertia of negative emotions, and that inertia of negative emotions aggravates the relation between negative emotions and workers' counterproductive work behaviour. [ABSTRACT FROM AUTHOR]

We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCREC. Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCREC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners’ cognitive load to keep their emotional experience at optimal levels to enhance learning.

Research on fine-grained dynamic psychological processes has increasingly come to rely on continuous self-report measures. Recent studies have extended continuous self-report methods to simultaneously collecting ratings on two dimensions of an experience. For all the variety of approaches, several limitations are inherent to most of them. First, current methods are primarily suited for bipolar, as opposed to unipolar, constructs. Second, respondents report on two dimensions using one hand, which may produce method driven error, including spurious relationships between the two dimensions. Third, two-dimensional reports have primarily been validated for consistency between reporters, rather than the predictive validity of idiosyncratic responses. In a series of tasks, the study reported here addressed these limitations by comparing a previously used method to a newly developed two-handed method, and by explicitly testing the validity of continuous two-dimensional responses. Results show that our new method is easier to use, faster, more accurate, with reduced method-driven dependence between the two dimensions, and preferred by participants. The validity of two-dimensional responding was also demonstrated in comparison to one-dimensional reporting, and in relation to post hoc ratings. Together, these findings suggest that our two-handed method for two-dimensional continuous ratings is a powerful and reliable tool for future research. [ABSTRACT FROM AUTHOR]