The role of automaticity and attention in neural processes underlying empathy for happiness, sadness, and anxiety.

Although many studies have examined the neural basis of empathy, relatively little is known about how empathic processes are affected by different attentional conditions. Thus, we examined whether instructions to empathize might amplify responses in empathy-related regions and whether cognitive load would diminish the involvement of these regions. Thirty-two participants completed a functional magnetic resonance imaging session assessing empathic responses to individuals experiencing happy, sad, and anxious events. Stimuli were presented under three conditions: watching naturally, actively empathizing, and under cognitive load. Across analyses, we found evidence for a core set of neural regions that support empathic processes (dorsomedial prefrontal cortex, DMPFC; medial prefrontal cortex, MPFC; temporoparietal junction, TPJ; amygdala; ventral anterior insula, AI; and septal area, SA). Two key regions-the ventral AI and SA-were consistently active across all attentional conditions, suggesting that they are automatically engaged during empathy. In addition, watching vs. empathizing with targets was not markedly different and instead led to similar subjective and neural responses to others' emotional experiences. In contrast, cognitive load reduced the subjective experience of empathy and diminished neural responses in several regions related to empathy and social cognition (DMPFC, MPFC, TPJ, and amygdala). The results reveal how attention impacts empathic processes and provides insight into how empathy may unfold in everyday interactions

Learning by Seeing by Doing: Arithmetic Word Problems

Learning by doing in pursuit of real-world goals has received much attention from education researchers but has been unevenly supported by mathematics education software at the elementary level, particularly as it involves arithmetic word problems. In this article, we give examples of doing-oriented tools that might promote children\u27s ability to see significant abstract structures in mathematical situations. The reflection necessary for such seeing is motivated by activities and contexts that emphasize affective and social aspects. Natural language, as a representation already familiar to children, is key in these activities, both as a means of mathematical expression and as a link between situations and various abstract representations. These tools support children\u27s ownership of a mathematical problem and its expression; remote sharing of problems and data; software interpretation of children\u27s own word problems; play with dynamically linked representations with attention to children\u27s prior connections; and systematic problem variation based on empirically determined level of difficulty

Civil-Military Relations in a Civilized State: Panama

This article traces and analyzes civil-military relations in Panama. After a brief overview of the role of the National Guard in the country politics, the article concentrates on political developments since the 1989 U.S. invasion to overthrow the Noriega regime and the subsequent elimination of the Panamanian military. The study seeks to shed light on political life in an armyless and politically and socially fractionalized country occupying a sensitive strategic location. The concluding part of the study speculates on the possibility that terrorism, domestic security concerns, and regional considerations may prompt Washington and Panamanian leaders to reverse the decision to abolish the country\u27s military institution

Causes and consequences of monodominance in tropical lowland forests

Journal ArticleTropical canopy dominance in lowland, well-drained forests by one plant species is a long-standing conundrum in tropical biology. Research now shows that dominance is not the result of one trait or mechanism. We suggest that the striking dominance of Gilbertiodendron dewevrei in the Ituri Forest of northeastern Congo is the result of a number of traits in adult trees that significantly modify the understory environment, making it difficult for other species to regenerate there

A Program Evaluation on the Effectiveness of Chronic Pain Management in a Rural Community

Chronic pain is a well-known, significant problem in the United States, affecting approximately 55% of adults in Western countries. Opioid use to manage chronic pain impacts individual’s social, psychological, and physiological health. The shortcomings of monotherapy, such as opioid use, have been led to extensive research to develop and assess the outcome of multi-disciplinary evidence-based programs. Rather than eliminating pain, interventions focus on pain management and improving quality of life, independence, and mobility. This quasiexperimental study examined the effectiveness of the Persistent Pain Program (PPP), an 8-week Cognitive Behavioral Treatment program and movement therapy intervention for patients living in a rural area of Oregon. Outcome data for 168 patients enrolled in this program between 2015 and 2017 were analyzed. Pre, post, and follow-up assessments included the Brief Pain Inventory (BPI), Patient Health Questionnaire-9 (PHQ-9), Pain Self Efficacy Questionnaire (PSEQ), the Oswestry Low Back Pain and Disability Questionnaire (OLBPD), and Tampa Scale of Kinesiphobia (TSK). Data were analyzed using a repeated measure MANOVA, to assess the differences from pre-intervention, through post intervention, and post follow-up. The results indicated that from pre-intervention to post-intervention most tests showed small to moderate improvements. Also, examining the period from post intervention through follow-up (assessed at greater than 6 months post-treatment) most measures indicated that patients were able to maintain their progress. Lastly, in comparison of pre-intervention and post-follow up, the results confirmed that the intervention showed small to moderate improvements on all outcome measures. The results suggest the PPP was associated with reduced pain severity and depressive symptoms and improved movement. Limitations and recommendations for future research are discussed

Bayesian regression discontinuity designs: Incorporating clinical knowledge in the causal analysis of primary care data

The regression discontinuity (RD) design is a quasi-experimental design that estimates the causal effects of a treatment by exploiting naturally occurring treatment rules. It can be applied in any context where a particular treatment or intervention is administered according to a pre-specified rule linked to a continuous variable. Such thresholds are common in primary care drug prescription where the RD design can be used to estimate the causal effect of medication in the general population. Such results can then be contrasted to those obtained from randomised controlled trials (RCTs) and inform prescription policy and guidelines based on a more realistic and less expensive context. In this paper we focus on statins, a class of cholesterol-lowering drugs, however, the methodology can be applied to many other drugs provided these are prescribed in accordance to pre-determined guidelines. NHS guidelines state that statins should be prescribed to patients with 10 year cardiovascular disease risk scores in excess of 20%. If we consider patients whose scores are close to this threshold we find that there is an element of random variation in both the risk score itself and its measurement. We can thus consider the threshold a randomising device assigning the prescription to units just above the threshold and withholds it from those just below. Thus we are effectively replicating the conditions of an RCT in the area around the threshold, removing or at least mitigating confounding. We frame the RD design in the language of conditional independence which clarifies the assumptions necessary to apply it to data, and which makes the links with instrumental variables clear. We also have context specific knowledge about the expected sizes of the effects of statin prescription and are thus able to incorporate this into Bayesian models by formulating informative priors on our causal parameters.Comment: 21 pages, 5 figures, 2 table