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ABSTRACTSome individuals devalue positivity previously associated with negativity (Winer & Salem, 2016). Positive emotions (e.g. happiness) may be seen as threatening and result in active avoidance of future situations involving positivity. Although some self-report measures can capture emotions of happiness-averse individuals, they are not always capable of capturing automatic processing. Thus, we examined the association between implicitly-assessed happiness and explicit (i.e. self-reported) fear of happiness in three studies. In Study 1, participants completed the Fear of Happiness Scale (FHS) and an implicit measure of emotions at four-time points over approximately one year. The implicit measure required participants to choose which emotion (i.e. anger, fear, happiness, sadness, or none) best corresponded to 20 individual Chinese characters. In Studies 2 and 3, we utilized an experimental design, implementing a mood induction to emphasise the relationship between explicit fear of happiness and implicitly-assessed happiness. Participants completed the FHS and chose which emotion they believed the artist tried to convey in 20 abstract images. Results indicated that greater self-reported fear of happiness was related to reduced implicit happiness. Findings from these studies provide compound evidence that individuals who hold negative views of positivity may process implicit happiness in a devaluative manner.

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OBJECTIVE/BACKGROUND: Prior research has emphasized the bidirectional relationships between sleep, stress, and affective states, such as depression. Given the inherent variability and fluctuations associated with sleep, assessing how sleep and affective variables function within a dynamic system may help further uncover possible causes and consequences of sleep disturbances, as well as find candidate targets for intervention. To this end, we examined dynamic relationships between self-reported stress, depressed mood, and clinically-relevant sleep parameters via temporal network analysis. METHODS: Participants were 401 nurses (92% female, 78% White, Mage = 39.47 years) who completed 14 days of sleep diaries incorporating self-reported stress and depression, as well as total sleep time, sleep efficiency, sleep onset latency, and wake after sleep onset. RESULTS AND CONCLUSIONS: Overall, total sleep time emerged as a highly influential variable in the context of "outstrength centrality," meaning total sleep time had numerous outward connections with other variables (e.g., stress and sleep efficiency). The high outstrength centrality of total sleep time suggests this variable is a source of activation within this dynamic system. Conversely, stress showed high "instrength centrality," suggesting this variable was highly impacted by other variables in the system, such as depressed mood and sleep efficiency. These findings emphasize the importance of assessing unfolding sleep processes within a naturalistic setting, and implicate the role of total sleep time in fueling depressed mood and stress. Discussion emphasizes implications of these results for understanding the connections between sleep, stress, and depression as well as clinical relevance of these findings.

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The use of exploratory network analysis has increased in psychopathology research over the past decade. A benefit of exploratory network analysis is the wealth of information it can provide; however, a single analysis may generate more inferences than what can be discussed in one manuscript (e.g., centrality indices of each node). This necessitates that authors choose which results to discuss in further detail and which to omit. Without a guide for this process, the likelihood of a biased interpretation is high. We propose that the integration of theory throughout the research process makes the interpretation of exploratory networks more manageable for the researcher and more likely to result in an interpretation that advances science. The goals of this paper are to differentiate between exploratory and confirmatory network analyses, discuss the utility of exploratory work, and provide a practical framework that uses theory as a guide to interpret exploratory network analyses.

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OBJECTIVE: Components of rumination, including brooding and reflection, as well as devaluating prospective positivity, may help maintain depressive symptoms. We examined these components together for the first time using network analysis. METHODS: We examined the robustness of rumination communities of closely related items in one network and then examined the interrelationships between rumination communities, devaluation of positivity, and depression, in a second network. RESULTS: Three rumination communities emerged, replicating findings of Bernstein et al. (2019). Within a dense network, nodes representing brooding, reflective pondering, and difficulty trusting positive feelings were most influential. In addition, the node representing the depressive symptom negative self-views shared strong edges with nodes representing devaluation of positivity and brooding. CONCLUSION: Brooding, reflective pondering, and elements of devaluing positivity are influential to depressive symptoms and may be important future experimental and therapeutic targets. Depressed individuals with negative self-views may engage in brooding and devalue their experience of positivity.

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OBJECTIVES: Reward devaluation theory (RDT) posits that some depressed individuals avoid positivity due to its previous association with negative outcomes. Behavioral indicators of avoidance of reward support RDT, but self-report indicators have yet to be examined discriminantly. Two candidate self-report measures were examined in relation to depression: negative affect interference (NAI), or the experience of negative affect in response to positivity, and fear of happiness, a fear of prospective happiness. METHOD: Participants completed measures assessing NAI, fear of happiness scale, and depression online via Amazon's Mechanical Turk at three time points (N = 375). Multilevel modeling examined the relationship between NAI, fear of happiness, and depressive symptoms longitudinally. RESULTS: NAI and fear of happiness were both positively associated with depressive symptoms. They both uniquely predicted depressive symptoms when included within the same model. CONCLUSIONS: These findings suggest that different conceptualizations of positivity avoidance are uniquely associated with depressive symptoms.

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Depression has been linked to multiple forms of aggressive behavior in college students; however, it is unclear which aspects of depression explain this connection. Anhedonia, defined as the loss of interest and/or pleasure in previously enjoyed activities, may provide unique information about relationships between depression and aggression. Using cross-sectional data from two independent samples of college students (N = 747 and N = 736 for Study 1 and Study 2, respectively), we examined whether anhedonia helped explain the relationship between broader depressive symptoms and different forms of aggressive and antisocial behavior. Anhedonia accounted for variance in both self-directed aggression and antisocial behavior independent of gender, hostility, anger, other depressive symptoms, and cognitive distortions (Study 2). In addition, there were significant indirect effects of depressive symptoms on self-directed aggression (Studies 1 and 2) and antisocial behavior (Study 2) via anhedonia. Hypotheses involving other-directed aggression received mixed support, with anhedonia atemporally associated with other-directed aggression independent of broader depressive symptoms in Study 1, but not in Study 2. The current findings suggest that anhedonia is an important individual difference that helps explain the relationship between depression and aggressive and antisocial acts and that anhedonia may be differentially associated with various types of aggressive and antisocial behavior.

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OBJECTIVE: Network analysis in psychology has ushered in a potentially revolutionary way of analyzing clinical data. One novel methodology is in the construction of temporal networks, models that examine directionality between symptoms over time. This paper provides context for how these models are applied to clinically-relevant longitudinal data. METHODS: We provide a survey of statistical and methodological issues involved in temporal network analysis, providing a description of available estimation tools and applications for conducting such analyses. Further, we provide supplemental R code and discuss simulations examining temporal networks that vary in sample size, number of variables, and number of time points. RESULTS: The following packages and software are reviewed: graphicalVAR, mlVAR, gimme, SparseTSCGM, mgm, psychonetrics, and the Mplus dynamic structural equation modeling module. We discuss the utility each procedure has for specific design considerations. CONCLUSION: We conclude with notes on resources for estimating these models, emphasizing how temporal networks best approximate network theory.

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Anhedonia has been implicated as a core symptom of depression and schizophrenia, and studying anhedonia has yielded a wide array of important findings aiding the understanding and identification of psychological disorders. However, anhedonia is a complex and multifaceted construct; indeed, the term anhedonia has been defined in psychological and psychiatric research as many different concepts, a number of which are theoretically and methodologically independent of one another. In this review alone, we discuss research that separates social aspects of anhedonia from the physical contexts of anhedonia, with the former emphasizing interpersonal relationships as important to anhedonic symptoms, and the latter emphasizing biological and brain-related impairment as potential causes of chronic anhedonia states. We highlight research that distinguishes between interest in (wanting) or experience of (liking) potential pleasure as definitions of anhedonia and also disambiguate methodologically and theoretically distinct ways of assessing 1) trait-level dispositional tendencies, 2) state-level cross-sectional assessments, and 3) symptom-based recent changes from baseline, all of which have been used to indicate anhedonia. Lastly, we describe cutting-edge translations of basic anhedonia research into treatment and discuss how different conceptualizations of anhedonia, guided by recent theoretical and methodological advances, have begun to usher in a science of anhedonia that is consistent with increasingly personalized assessment and treatment. We conclude with a note for future research, emphasizing that continued application of theoretically based operationalizations of anhedonia and sound design are paramount to continue the recent progress toward meaningful and specific use of the anhedonia construct in clinical research.

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Fear of positive evaluation (FPE) is experiencing dread during real or potential praise. FPE is associated with social anxiety, but its relation to depressive symptoms is unclear. Anhedonia is a core symptom of depression related to symptoms of anxiety in cross-sectional research. The current study investigated the indirect effect of FPE on depressive symptoms via anhedonia over time. One-hundred ninety-six participants completed three waves of questionnaires over a total timespan of approximately four months via Amazon's Mechanical Turk, including measures of FPE, depressive symptoms, and anticipatory and consummatory anhedonia. Findings indicated that anticipatory anhedonia at Time 2 mediated the relationship between FPE at Time 1 and depressive symptoms at Time 3. Consummatory anhedonia, however, did not. Each model was contextualised by accounting for prospective covarying relationships, such as depressive symptoms predicting the same symptoms at later waves. The constellation of findings is considered within a reward devaluation framework.

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