Daily life affective dynamics as transdiagnostic predictors of mental health symptoms:An ecological momentary assessment study

BackgroundAffective dynamics have been identified as a correlate of a broad span of mental health issues, making them key candidate transdiagnostic factors. However, there remains a lack of knowledge about which aspects of affective dynamics – especially as they manifest in the course of daily life – relate to a general risk for mental health issues versus specific symptoms. MethodsWe leverage an ecological momentary assessment (EMA) study design with four measures per day over a two-week period to explore how negative affect levels, inertia, lability, and reactivity to provocation and stress in the course of daily life relate to mental health symptoms in young adults (n= 256) in the domains of anxiety, depression, psychosis-like symptoms, behaviour problems, suicidality, and substance use. ResultsDynamic structural equation modelling (DSEM) suggested that negative affect levels in daily life were associated with depression, anxiety, indirect and proactive aggression, psychosis, anxiety, and self-injury; negative affective lability was associated with depression, physical aggression, reactive aggression, suicidal ideation, and ADHD symptoms; negative affective inertia was associated with depression, anxiety, physical aggression, and cannabis use; and emotional reactivity to provocation was related to physical aggression. LimitationsThe cross-sectional design, the limited span of mental health issues included, and the convenience nature and small size of the sample are limitations.ConclusionsFindings suggest that a subset of mental health symptoms have shared negative affective dynamics patterns. Longitudinal research is needed to rigorously examine the directionality of the effects underlying the association between affective dynamics and mental health issues

Open X-Embodiment:Robotic learning datasets and RT-X models

Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, this has led to a consolidation of pretrained models, with general pretrained backbones serving as a starting point for many applications. Can such a consolidation happen in robotics? Conventionally, robotic learning methods train a separate model for every application, every robot, and even every environment. Can we instead train "generalist" X-robot policy that can be adapted efficiently to new robots, tasks, and environments? In this paper, we provide datasets in standardized data formats and models to make it possible to explore this possibility in the context of robotic manipulation, alongside experimental results that provide an example of effective X-robot policies. We assemble a dataset from 22 different robots collected through a collaboration between 21 institutions, demonstrating 527 skills (160266 tasks). We show that a high-capacity model trained on this data, which we call RT-X, exhibits positive transfer and improves the capabilities of multiple robots by leveraging experience from other platforms. The project website is robotics-transformer-x.github.io

Daily life affective dynamics as transdiagnostic predictors of mental health symptoms : An ecological momentary assessment study

Affective dynamics have been identified as a correlate of a broad span of mental health issues, making them key candidate transdiagnostic factors. However, there remains a lack of knowledge about which aspects of affective dynamics - especially as they manifest in the course of daily life - relate to a general risk for mental health issues versus specific symptoms. We leverage an ecological momentary assessment (EMA) study design with four measures per day over a two-week period to explore how negative affect levels, inertia, lability, and reactivity to provocation and stress in the course of daily life relate to mental health symptoms in young adults (n = 256) in the domains of anxiety, depression, psychosis-like symptoms, behaviour problems, suicidality, and substance use. Dynamic structural equation modelling (DSEM) suggested that negative affect levels in daily life were associated with depression, anxiety, indirect and proactive aggression, psychosis, anxiety, and self-injury; negative affective lability was associated with depression, physical aggression, reactive aggression, suicidal ideation, and ADHD symptoms; negative affective inertia was associated with depression, anxiety, physical aggression, and cannabis use; and emotional reactivity to provocation was related to physical aggression. The cross-sectional design, the limited span of mental health issues included, and the convenience nature and small size of the sample are limitations. Findings suggest that a subset of mental health symptoms have shared negative affective dynamics patterns. Longitudinal research is needed to rigorously examine the directionality of the effects underlying the association between affective dynamics and mental health issues