Multilevel Modeling of Interval-Contingent Data In Neuropsychology Research Using the \u3ci\u3eImerTest\u3c/i\u3e Package In R

Intensive longitudinal research designs are becoming more common in the field of neuropsychology. They are a powerful approach to studying development and change in naturally occurring phenomena. However, to fully capitalize on the wealth of data yielded by these designs, researchers have to understand the nature of multilevel data structures. The purpose of the present article is to describe some of the basic concepts and techniques involved in modeling multilevel data structures. In addition, this article serves as a step-by-step tutorial to demonstrate how neuropsychologists can implement basic multilevel modeling techniques with real data and the R package, lmerTest. R may be an ideal option for some empirical scientists, applied statisticians, and clinicians, because it is a free and open-source programming language for statistical computing and graphics that offers a flexible and powerful set of tools for analyzing data. All data and code described in the present article have been made publicly available

The Optimal Calibration Hypothesis: How Life History Modulates the Brain\u27s Social Pain Network

A growing body of work demonstrates that the brain responds similarly to physical and social injury. Both experiences are associated with activity in the dorsal anterior cingulate cortex (dACC) and anterior insula. This dual functionality of the dACC and anterior insula underscores the evolutionary importance of maintaining interpersonal bonds. Despite the weight that evolution has placed on social injury, the pain response to social rejection varies substantially across individuals. For example, work from our lab demonstrated that the brain\u27s social pain response is moderated by attachment style: anxious-attachment was associated with greater intensity and avoidant-attachment was associated with less intensity in dACC and insula activation. In an attempt to explain these divergent responses in the social pain network, we propose the optimal calibration hypothesis, which posits variation in social rejection in early life history stages shifts the threshold of an individual\u27s social pain network such that the resulting pain sensitivity will be increased by volatile social rejection and reduced by chronic social rejection. Furthermore, the social pain response may be exacerbated when individuals are rejected by others of particular importance to a given life history stage (e.g., potential mates during young adulthood, parents during infancy and childhood)

Who Is Most Vulnerable to Social Rejection? The Toxic Combination of Low Self-Esteem and Lack of Negative Emotion Differentiation on Neural Responses to Rejection

People have a fundamental need to belong that, when satisfied, is associated with mental and physical well-being. The current investigation examined what happens when the need to belong is thwarted—and how individual differences in self-esteem and emotion differentiation modulate neural responses to social rejection. We hypothesized that low self-esteem would predict heightened activation in distress-related neural responses during a social rejection manipulation, but that this relationship would be moderated by negative emotion differentiation—defined as adeptness at using discrete negative emotion categories to capture one\u27s felt experience. Combining daily diary and neuroimaging methodologies, the current study showed that low self-esteem and low negative emotion differentiation represented a toxic combination that was associated with stronger activation during social rejection (versus social inclusion) in the dorsal anterior cingulate cortex and anterior insula—two regions previously shown to index social distress. In contrast, individuals with greater negative emotion differentiation did not show stronger activation in these regions, regardless of their level of self-esteem; fitting with prior evidence that negative emotion differentiation confers equanimity in emotionally upsetting situations

INTERPERSONAL RELATIONS AND GROUP PROCESSES Putting the Brakes on Aggression Toward a Romantic Partner: The Inhibitory Influence of Relationship Commitment

Why do people behave aggressively toward romantic partners, and what can put the brakes on this aggression? Provocation robustly predicts aggression in both intimate and nonintimate relationships. Four methodologically diverse studies tested the hypothesis that provocation severity and relationship commitment interact to predict aggression toward one's romantic partner, with the aggression-promoting effects of provocation diminishing as relationship commitment increases. Across all four studies, commitment to one's romantic relationship inhibited aggression toward one's partner when individuals were severely (but not mildly) provoked. Study 4 tested the hypothesis that this Partner Provocation ϫ Commitment interaction effect would be strong among individuals high in dispositional tendencies toward retaliation but weak (perhaps even nonexistent) among individuals low in such tendencies. Discussion emphasizes the importance of understanding instigating, impelling, and inhibiting processes in the perpetration of aggression toward intimate partners. Keywords: romantic relationships, commitment, aggression, I 3 theory Although romantic relationships often begin with chocolates and roses, eventually thorns are sure to emerge. Indeed, precisely because of the deep interdependence that characterizes these relationships, romantic partners have a particularly pronounced capacity to be infuriating. Whether by flirting with others, criticizing our flaws, thoughtlessly neglecting our needs and desires, or by other omissions and commissions, romantic partners can sometimes provoke angry responses. Such provocation frequently triggers an urge toward retaliation, perhaps even toward aggression. When will provoked people aggress toward their romantic partner, and what might put the brakes on their aggression? In the current investigation, we test the hypothesis that partner provocation increases aggressive tendencies toward one's partner, especially among individuals who are weakly (vs. strongly) committed to their partner. The logic underlying this prediction is that partner provocation frequently triggers an urge toward aggressive retaliation but that relationship commitment helps individuals override this urge. Partner Provocation in Intimate Relationships Although people typically expect that romantic relationships will be rewarding, most individuals experience some amount of conflict with their romantic partner. Indeed, conflict is "an inevitable-though often unanticipated-feature of close relationships. The strong, frequent, and diverse bonds between [romantic partners] set the stage for conflicting interests to surface&quot

An unclear self leads to poor mental health: Self-concept confusion mediates the association of loneliness with depression

Past research has established that loneliness is associated with both self-concept confusion and depression. The present work ties these disparate lines of research together by demonstrating that self-concept confusion mediates the relationship between loneliness and depression. Three studies, one cross-sectional and two longitudinal, supported this hypothesis. Moreover, the model was supported both in samples of dating and married couples and in samples of noncouples. This research contributes to a greater understanding of why people who feel socially disconnected have poor mental health. Understanding this mechanism has important implications for strategies targeting the early prevention of depression and improving mental health outcomes

Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

Background: In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods: GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings: Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation: As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and developm nt investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding: Bill & Melinda Gates Foundation. © 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 licens

Threatened Social Needs After Exclusion in Undergraduate Students With Varying Degrees of Attention Switching Difficulties

Individuals on the autism spectrum seem to be at higher risk social exclusion which can have serious psychological and physiological consequences. The current study examined how individuals with varying traits of autism are impacted by social exclusion in terms of threats to their social needs. Undergraduates (N=185) completed a self-report measure of autistic traits, were randomly assigned to be included or excluded in a virtual ball-tossing game (i.e., Cyberball), and threats to their social needs were assessed. Results indicated that attention switching deficits impact how individuals experience social exclusion. Better understanding of the way need threat manifests itself illuminates how individuals with varying levels of autistic traits may respond to a common type of bullying they may experience: social exclusion

Multilevel Modeling of Interval-Contingent Data in Neuropsychology Research Using the lmerTest Package in R

Intensive longitudinal research designs are becoming more common in the field of neuropsychology. They are a powerful approach to studying development and change in naturally occurring phenomena. However, to fully capitalize on the wealth of data yielded by these designs, researchers have to understand the nature of multilevel data structures. The purpose of the present article is to describe some of the basic concepts and techniques involved in modeling multilevel data structures. In addition, this article serves as a step-by-step tutorial to demonstrate how neuropsychologists can implement basic multilevel modeling techniques with real data and the R package, lmerTest. R may be an ideal option for some empirical scientists, applied statisticians, and clinicians, because it is a free and open-source programming language for statistical computing and graphics that offers a flexible and powerful set of tools for analyzing data. All data and code described in the present article have been made publicly available