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Dynamical interplays in emotions have been investigated using vector autoregressive (VAR) models, whose estimates can be used to cluster participants into unknown groups. The present study evaluated a clustering algorithm, the alternating least square (ALS) algorithm, for accuracy in predicting individual group membership. We systematically manipulated (a) the number of variables in a model, (b) the size of group differences in regression coefficients, and (c) the number of regression coefficients that vary across the groups (i.e., effective features). The ALS algorithm works reliably when there are at least 5 effective features with very large group differences in a 5-variable model; and 9 effective features with very large group differences in a 10-variable model. These findings suggest that the ALS algorithm is sensitive to group differences that are present only in several coefficients of a VAR model, but that the group differences have to be large. We also found that the ALS algorithm outperforms another clustering method, Gaussian mixture modeling. The ALS algorithm was further evaluated with unbalanced sample sizes between groups and with a greater number of groups in data (Study 2). A real data application was provided to illustrate how to interpret the detected group differences (Study 3).

The Autobiographical Memory Test (AMT) has been central in psychopathological studies of memory dysfunctions, as reduced memory specificity or overgeneralised autobiographical memory has been recognised as a hallmark vulnerability for depression. In the AMT, participants are asked to generate specific memories in response to emotional cue words, and their responses are scored by human experts. Because the manual coding takes some time, particularly when analysing a large dataset, recent studies have proposed computerised scoring algorithms. These algorithms have been shown to reliably discriminate between specific and non-specific memories of English-speaking children and Dutch- and Japanese-speaking adults. The key limitation is that the algorithm is not developed for English-speaking adult memories, which may cover a wider range of vocabulary that the existing algorithm for English-speaking child memories cannot process correctly. In the present study, we trained a new support vector machine to score memories of English-speaking adults. In a performance test (predicting memory specificity against human expert coding), the adult-memory algorithm outperformed the child-memory variant. In another independent performance test, the adult-memory algorithm showed robust performances to score memories that were generated in response to a different set of cues. These results suggest that the adult-memory algorithm reliably scores memory specificity.

Research has shown that how well people can differentiate between different emotional states is an essential requirement for adaptive emotion regulation. People with low levels of emotion differentiation tend to be more vulnerable to develop emotional disorders. Although we know quite a lot about the correlates of emotion differentiation, research on factors or interventions which could improve emotion differentiation skills is scarce. Here, we hypothesize, and study empirically, whether a mindfulness-based intervention (MBI) may impact the differentiation of negative and positive emotions. A within-subjects pre-, post-, and follow-up design involving experience sampling was used. At each phase participants reported their current emotions and mindfulness skills up to 40 times across 4 consecutive days using smartphones. Multilevel modeling showed a significant improvement in negative emotion differentiation postintervention and at 4 months of follow-up, and a significant improvement in positive emotion differentiation at 4 months follow-up. The improvement in negative emotion differentiation, however, was no longer significant when controlling for levels of negative affect. A time-lagged mediation model showed that posttreatment changes in mindfulness skills mediated subsequent changes in negative emotion differentiation, also when controlling for levels of negative affect. These results suggest that MBI is a promising approach to improve people's emotion differentiation skills. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Attentional bias toward threatening stimuli is recognized as 1 of the most important cognitive vulnerability factors for anxiety. However, the association between anxious attention and negative moods has not been fully elucidated, particularly in terms of the effect of such bias on mood fluctuations in daily life. We examined the associations between attentional bias and emotional dynamics under a daily life setting. Participants (46 university students) completed the dot-probe task in the laboratory, after which they entered a 6-day experience sampling measurement to assess temporal fluctuations of momentary levels of anxious mood and occurrence of stressful events. The results showed that attentional bias was significantly correlated with increased temporal instability of anxious mood. Furthermore, attentional bias was also associated with enhanced emotional reactivity immediately after occurrence of the stressful event, but not with either the sensitivity to detect stressors or to the recovery process aimed at calming the stress-induced anxious mood on a longer time scale. These findings suggest that attentional bias is associated with the acute impact of a stressor on moods, as evidenced by the increased temporal instability and stress-reactivity of anxious mood. (PsycINFO Database Record, ((c) 2018 APA, all rights reserved).)

Previous studies have shown that attentional bias modification (ABM) is effective in reducing negative attentional biases. However, the mechanisms underlying how ABM effectively reduces negative attentional biases are still unclear. In the present study, we conducted an ABM procedure that included a 3-day training session with a sample of nonclinical participants (N = 40) to investigate the effect of ABM on emotional and nonemotional attentional biases. Participants completed a modified dot-probe task with 2 different instructions (explicit or standard) during the training; their attentional biases were tested before and after the training. Only participants trained with explicit instructions showed a reduction in negative attentional biases in dot-probe task and an improvement in attentional disengagement from negative stimuli in gap-overlap task. On the other hand, attention toward nonemotional stimuli was only marginally improved by training with both explicit and standard instructions. These results indicate that explicit instructions may promote ABM training., ((c) 2015 APA, all rights reserved).)

Although social support facilitates coping and recovering from stressful life events, people do not always get the support that they need. Prior research suggests that the way one talks about stressful events to others may influence the support they receive. Given that people are increasingly relying on online communities for social support, this study adopted a person-centered approach (latent profile analysis) to examine how narrative variables related to the motivational themes, emotional content, and organizational structure of randomly sampled support-seeking messages (N = 495) posted on Reddit (r/Anxiety and r/Depression) influenced the quantity (number of comments and post score) and quality (type of support in comments) of support that they received. We identified five distinct narrative profiles of support-seeking posts, which in turn differentially predicted the quality, but not quantity, of social support people received. While commenters provided high levels of emotional support to all forms of posts, we found that coherence was an important determinant of esteem support. A combination of coherence, as well as agency and affective tone, were important determinants of instrumental, informational, and network support. The ways in which one talks about their problems influence the way others support them. [ABSTRACT FROM AUTHOR]

The underlying mechanisms of the effectiveness of mindfulness-based interventions for emotional well-being remain poorly understood. Here, we examined the potential mediating effects of cognitive reactivity and self-compassion on symptoms of depression, anxiety and stress using data from an earlier randomised controlled school trial. A moderated time-lagged mediation model based on multilevel modelling was used to analyse the data. The findings showed that post-treatment changes in cognitive reactivity and self-coldness, an aspect of self-compassion, mediated subsequent changes in symptoms of depression, anxiety and stress. These results suggest that cognitive reactivity and self-coldness may be considered as transdiagnostic mechanisms of change of a mindfulness-based intervention programme for youth. [ABSTRACT FROM AUTHOR]

Purpose: Adolescent and young adult cancer (AYAC) survivors show an elevated risk of distress. Targeted psychosocial interventions for this distinct population are needed. This study examined the potential efficacy of a mindfulness-based intervention (MBI) to alleviate emotional distress and improve quality of life (QoL) in AYAC survivors. Methods: Participants were 16 AYAC survivors, aged 14-24, who had completed acute medical treatment. A two-baseline (8 and 1 week before the intervention), post- (1 week after the intervention) and 3 months follow-up within-subjects design was used. Each participant completed two baseline assessments, followed by an 8-week MBI. The primary outcome variables were emotional distress and QoL. Secondary outcomes were cognitive vulnerability factors and mindfulness skills. Results: Multilevel modeling showed (1) a significant reduction in emotional distress and improvement in QoL at 3 months of follow-up, (2) a significant reduction in negative attitudes toward self (i.e., a cognitive vulnerability factor), and (3) a significant improvement in mindfulness skills. Conclusion: MBI is a promising approach that is used to treat emotional distress and to improve QoL in AYAC survivors. Further research using randomized controlled trials is needed to generalize these findings. Trial registration information: ; NTR4358. [ABSTRACT FROM AUTHOR]

The self-positivity bias, which is inherent to healthy people, is known to be blunted in depression. The lack of positive or excessive negative self-reference is considered to be a potential mechanism underlying depressive rumination. However, the motivational factors that drive people to approach and avoid emotional self-related materials are still unclear. Therefore, we measured intrinsic motivation that is associated with emotional self-references by using a reward-based decision-making task (pay-per-view paradigm). Forty-nine undergraduates completed two tasks in which they were asked to choose between negative vs. positive references (Task 1) and self vs. other references (Task 2) for variable monetary rewards. Participants with lower levels of depressive symptoms showed a self-positivity bias, sacrificing rewards for the opportunity to engage in positive self-reference, whereas those with higher levels of depressive symptoms had no specific preference for either negative or positive self-reference (Task 1). However, all participants sacrificed monetary rewards for the opportunity for self-reference versus other reference, regardless of the symptom level or the primed valence (Task 2). Together, these findings suggest that depressive cognition could be characterised by the lack of intrinsic motivation for positive self-reference, which is attributable to the biased valence selection, but not to self–other preferences. [ABSTRACT FROM AUTHOR]

We investigated the effects of adaptive and maladaptive forms of self-focus-specifically, self-reflection and self-rumination-on the relationship between depressed mood and everyday problem-solving behavior. Although previous research has consistently suggested that self-rumination disturbs problem solving and self-regulatory processes, thereby aggravating depressive symptoms, the association between self-reflection, problem solving, and its emotional consequences has not been demonstrated. Therefore, we assessed whether self-reflection can facilitate the emotion regulation function of problem solving through a daily diary method. Thirty-nine Japanese undergraduate and graduate students recorded daily depressed mood, the most stressful problem encountered each day, and whether they utilized problem-solving behaviors for seven consecutive days. Multilevel model analyses showed that individuals with higher levels of self-reflection reported lower depressed moods after enacting problem-solving behaviors, even if the problem that they had on that day was highly stressful. These results suggest that self-reflection enhances the mood regulation function of everyday problem-solving behavior, and may contribute to mental well-being and resilience to stress. [ABSTRACT FROM AUTHOR]

A "sense of agency" involves a contemporaneous experience that the "self" causes the actions performed by the "self" (i.e., "I am the one who causes my actions"). This may comprise the main component of selfconsciousness. The present research focuses on the development of a questionnaire to investigate the subjective aspects of a sense of agency. We selected items from the extant relevant measures and from previous empirical studies, and conducted four longitudinal surveys with additional scales. Statistical computations confirmed the validity and reliability of the Sense of Agency Scale (SOAS), consisting of seventeen items involving three factors. Furthermore, the results indicated that these three factors might be organized hierarchically, with each factor showing a unique relationship with emotional or social traits. This novel finding, emerging from the Sense of Agency Scale, would have been difficult to obtain via traditional empirical studies. [ABSTRACT FROM AUTHOR]