Research has demonstrated that stressors play a critical role in the development of generalized anxiety disorder (GAD), social anxiety disorder (SAD), and major depressive disorder (MDD). Separately, deficits in positive affect (PA) have been identified in GAD, SAD, and MDD. Whereas previous research has linked the buffering effects of PA in chronic illness, such effects have yet to be investigated for chronic stressors and emotional disorder–related symptom severity. The purpose of the present study was to examine PA as a moderator of chronic interpersonal and noninterpersonal stress on GAD, SAD, and MDD symptom severity. Using a multilevel statistical approach with a sample of adolescents and young adults ( N = 463), PA was found to moderate significantly the relationship between chronic interpersonal stress and symptom severity for MDD and SAD. Findings suggest that in times of chronic interpersonal stress, higher PA may serve as a buffer from development of SAD and MDD symptoms.

Emotional expressions are an essential element of human interactions. Recent work has increasingly recognised that emotional vocalisations can colour and shape interactions between individuals. Here we present data on the psychometric properties of a recently developed database of authentic non-linguistic emotional vocalisations from human adults and infants [the Oxford Vocal ‘OxVoc’ Sounds Database, (Parsons, Young, Craske, Stein, & Kringelbach, 2014)]. In a large sample (n=562), we demonstrate that adults can reliably categorise these sounds (as ‘positive’, ‘negative’ or ‘sounds with no emotion’), and rate valence in these sounds consistently over time. In an extended sample (n=945, including the initial n=562), we also investigated a number of individual difference factors in relation to valence ratings of these vocalisations. Results demonstrated small but significant effects of: i) symptoms of depression and anxiety with more negative ratings of adult neutral vocalisations (R2 = .011 and R2 = .008 respectively) and ii) gender differences in perceived valence such that female listeners rated adult neutral vocalisations more positively and infant cry vocalisations more negatively than male listeners (R2 = .021, R2 = .010 respectively). Of note, we did not find evidence of negativity bias among other affective vocalisations or gender differences in perceived valence of adult laughter, adult cries, infant laughter or infant neutral vocalisations. Together, these findings largely converge with factors previously shown to impact processing of emotional facial expressions, suggesting a modality-independent impact of depression, anxiety and listener gender, particularly among vocalisations with more ambiguous valence.

Exposure, the repeated and systematic confrontation with feared stimuli, is a central component of cognitive behavior therapy (CBT) for anxiety and threatrelated disorders. Meta-analyses of randomized controlled trials over the past several decades have demonstrated very large effect sizes for exposure therapy for anxiety disorders, whether alone or combined with coping strategies such as cognitive reappraisal or breathing/relaxation training (Cuijpers, Cristea, Karyotaki, Reijnders, & Huibers, 2016). However, although the majority of individuals improve within 10 to 20 weekly sessions of typical treatment trials, only approximately 55% achieve normative functioning (Loerinc et al., 2015), and a number experience a return of fear, defined as resurgence of fear from the end of exposure therapy to follow-up testing with the same object that was targeted during exposure therapy.Over recent decades, our fundamental knowledge of basic fear learning processes has significantly evolved and has offered an explanation for return of fear and its malignant nature. These advancements offer important treatment implications and call for clinicians and researchers to adopt an advanced theoretical understanding of the mechanisms underlying exposure-based treatments based in modern associative fear learning. Within the updated inhibitory learning model of exposure, extinction is posited to be the critical process that results in long-term reductions of fear (

Machine learning (ML) has been introduced into the medical field as a means to provide diagnostic tools capable of enhancing accuracy and precision while minimizing laborious tasks that require human intervention. There is mounting evidence that the technology fueled by ML has the potential to detect, and substantially improve treatment of complex mental disorders such as depression. We developed a framework capable of detecting depression with minimal human intervention: AiME (Artificial Intelligence Mental Evaluation). AiME consists of a short human-computer interactive evaluation and artificial intelligence, namely deep learning, and can predict whether the participant is depressed or not with satisfactory performance. Due to its ease of use, this technology can offer a viable tool for mental health professionals to identify symptoms of depression, thus enabling a faster preventative intervention. Furthermore, it may alleviate the challenge of interpreting highly nuanced physiological and behavioral biomarkers of depression by providing a more objective evaluation.

Pretreatment deficits in disengaging attention from threat may promote better and more durable response to CBT for a range anxiety disorders.