Multifaceted consequences of visual distraction during natural behaviour.

Visual distraction is a ubiquitous aspect of everyday life. Studying the consequences of distraction during temporally extended tasks, however, is not tractable with traditional methods. Here we developed a virtual reality approach that segments complex behaviour into cognitive subcomponents, including encoding, visual search, working memory usage, and decision-making. Participants copied a model display by selecting objects from a resource pool and placing them into a workspace. By manipulating the distractibility of objects in the resource pool, we discovered interfering effects of distraction across the different cognitive subcomponents. We successfully traced the consequences of distraction all the way from overall task performance to the decision-making processes that gate memory usage. Distraction slowed down behaviour and increased costly body movements. Critically, distraction increased encoding demands, slowed visual search, and decreased reliance on working memory. Our findings illustrate that the effects of visual distraction during natural behaviour can be rather focal but nevertheless have cascading consequences.

The relationship between cognitive and affective control and adolescent mental health.

Background: Cognitive control problems have been implicated in the etiology and maintenance of mental health problems, including depression, in adults. Studies in adolescents have been more equivocal, with some showing changes in cognitive control in adolescents with mental health problems, whereas others fail to show an association. This study examines whether adolescent mental health is associated with affective control, the application of cognitive control in affective contexts, which shows more protracted development than cognitive control. Methods: The present study investigated the association of cognitive and affective control with depressive symptomatology and self-reported diagnostic history of mental health problems in adolescents. The study included 1929 participants (M age = 13.89) from the Future Proofing Study (N = 6,388, 11-16 years), who completed affective (incl., affective stimuli) and/or cognitive (incl., neutral stimuli) versions of a working memory (backward digit-span) and/or shifting (card-sorting) task at least once within 3 weeks of assessing mental health. Results: Poorer working memory was associated with greater depressive symptomatology in adolescents (ß = -0.06, p = .004), similarly across cognitive and affective control conditions (ß = -0.02, p = .269). Adolescents with self-reported diagnostic history of mental health problems had significantly poorer shifting ability in affective compared to cognitive control conditions (b = 0.05, p = .010), whereas for adolescents with no self-reported diagnoses, shifting ability did not differ between conditions (b = -0.00, p = .649). Conclusions: The present analyses suggest that working memory difficulties, in particular, may be associated with the experience of current depressed mood in adolescents. Problems with affective shifting may be implicated in a range of mental health problems in adolescents. Given the ubiquitous need for efficient cognitive functioning in daily life, enhancing cognitive and affective control in adolescents may be a promising means of improving functioning across a range of domains, including affective functioning, and by extension, adolescent mental health.

Using XR (Extended Reality) for Behavioral, Clinical, and Learning Sciences Requires Updates in Infrastructure and Funding.

Extended reality (XR, including augmented and virtual reality) creates a powerful intersection between information technology and cognitive, clinical, and education sciences. XR technology has long captured the public imagination, and its development is the focus of major technology companies. This article demonstrates the potential of XR to (1) deliver behavioral insights, (2) transform clinical treatments, and (3) improve learning and education. However, without appropriate policy, funding, and infrastructural investment, many research institutions will struggle to keep pace with the advances and opportunities of XR. To realize the full potential of XR for basic and translational research, funding should incentivize (1) appropriate training, (2) open software solutions, and (3) collaborations between complementary academic and industry partners. Bolstering the XR research infrastructure with the right investments and incentives is vital for delivering on the potential for transformative discoveries, innovations, and applications.

Association of Antenatal COVID-19-Related Stress With Postpartum Maternal Mental Health and Negative Affectivity in Infants.

Importance: Antenatal stress is a significant risk factor for poor postpartum mental health. The association of pandemic-related stress with postpartum outcomes among mothers and infants is, however, less well understood. Objective: To examine the association of antenatal COVID-19-related stress with postpartum maternal mental health and infant outcomes. Design, Setting, and Participants: This cohort study was conducted among 318 participants in the COVID-19 Risks Across the Lifespan study, which took place in Australia, the UK, and the US. Eligible participants reported being pregnant at the first assessment wave between May 5 and September 30, 2020, and completed a follow-up assessment between October 28, 2021, and April 24, 2022. Main Outcomes and Measures: COVID-19-related stress was assessed with the Pandemic Anxiety Scale (score range, 0-4, with higher scores indicating greater COVID-19-related stress). The 8-item Patient Health Questionnaire (score range, 0-3, with higher scores indicating more frequent symptoms of depression) was used to measure maternal depression at each time point, and the 7-item General Anxiety Disorder scale (score range, 0-3, with higher scores indicating more frequent symptoms of anxiety) was used to measure generalized anxiety at each time point. At follow-up, postpartum distress was assessed with the 10-item Postpartum Distress Measure (score range, 0-3, with higher scores indicating greater postpartum distress), and infant outcomes (negative and positive affectivity and orienting behavior) were captured with the Infant Behavior Questionnaire (score range, 1-7, with higher scores indicating that the infant exhibited that affect/behavior more frequently). Results: The study included 318 women (mean [SD] age, 32.0 [4.6] years) from Australia (88 [28%]), the US (94 [30%]), and the UK (136 [43%]). Antenatal COVID-19-related stress was significantly associated with maternal postpartum distress (ß = 0.40 [95% CI, 0.28-0.53]), depression (ß = 0.32 [95% CI, 0.23-0.41]), and generalized anxiety (ß = 0.35 [95% CI, 0.26-0.44]), as well as infant negative affectivity (ß = 0.45 [95% CI, 0.14-0.76]). The findings remained consistent across a range of sensitivity analyses. Conclusions and Relevance: The findings of this cohort study suggest that targeting pandemic-related stressors in the antenatal period may improve maternal and infant outcomes. Pregnant individuals should be classified as a vulnerable group during pandemics and should be considered a public health priority, not only in terms of physical health but also mental health.

Estimating power in (generalized) linear mixed models: An open introduction and tutorial in R.

Mixed-effects models are a powerful tool for modeling fixed and random effects simultaneously, but do not offer a feasible analytic solution for estimating the probability that a test correctly rejects the null hypothesis. Being able to estimate this probability, however, is critical for sample size planning, as power is closely linked to the reliability and replicability of empirical findings. A flexible and very intuitive alternative to analytic power solutions are simulation-based power analyses. Although various tools for conducting simulation-based power analyses for mixed-effects models are available, there is lack of guidance on how to appropriately use them. In this tutorial, we discuss how to estimate power for mixed-effects models in different use cases: first, how to use models that were fit on available (e.g. published) data to determine sample size; second, how to determine the number of stimuli required for sufficient power; and finally, how to conduct sample size planning without available data. Our examples cover both linear and generalized linear models and we provide code and resources for performing simulation-based power analyses on openly accessible data sets. The present work therefore helps researchers to navigate sound research design when using mixed-effects models, by summarizing resources, collating available knowledge, providing solutions and tools, and applying them to real-world problems in sample sizing planning when sophisticated analysis procedures like mixed-effects models are outlined as inferential procedures.