Person-specific theory of mind in medial pFC

Although research on theory of mind has strongly implicated the dorsomedial pFC (incuding medial BA 8 and BA 9), the unique contributions of medial pFC (MPFC; corresponding to medial BA 10) to mentalizing remain uncertain. The extant literature has considered the possibility that these regions may be specialized for self-related cognition or for reasoning about close others, but evidence for both accounts has been inconclusive. We propose a novel theoretical framework: MPFC selectively implements "person-specific theories of mind" (ToMp) representing the unique, idiosyncratic traits or attributes of well-known individuals. To test this hypothesis, we used fMRI to assess MPFC responses in Democratic and Republican participants as they evaluated more or less subjectively well-known political figures. Consistent with the ToMp account, MPFC showed greater activity to subjectively well-known targets, irrespective of participants' reported feelings of closeness or similarity. MPFC also demonstrated greater activity on trials in which targets (whether politicians or oneself) were judged to be relatively idiosyncratic, making a generic theory of mind inapplicable. These results suggest that MPFC may supplement the generic theory of mind process, with which dorsomedial pFC has been associated, by contributing mentalizing capacities tuned to individuated representations of specific well-known others

Disconfirmation modulates the neural correlates of the false consensus effect: A parametric modulation approach

The false consensus effect (FCE) - the tendency to (erroneously) project our attitudes and opinions onto others - is an enduring bias in social reasoning with important societal implications. In this fMRI investigation, we examine the neural correlates of within-subject variation in consensus bias on a variety of social and political issues. Bias demonstrated a strong association with activity in brain regions implicated in self-related cognition, mentalizing, and valuation. Importantly, however, recruitment of these regions predicted consensus bias only in the presence of social disconfirmation, in the form of feedback discrepant with participants' own attitudes. These results suggest that the psychological and neural mechanisms underlying the tendency to project attitudes onto others are crucially moderated by motivational factors, including the desire to affirm the normativity of one's own position. This research complements social psychological theorizing about the factors contributing to the FCE, and further emphasizes the role of motivated cognition in social reasoning

A Descriptive Study of the Case of Eaveston School District: Core Values from Deficit-Based to Asset-Based

A growing body of research has linked educational leadership and student achievement; however, the oppression of students of diverse race, ethnicity, and social class has perpetuated inequities and educational gaps for decades across the United States. Some educational leaders who care deeply about equity and social justice are examining their core values, behaviors, and beliefs, as well as their organization’s policies and practices to identify and implement knowledge and skills that disrupt the inequities producing educational and opportunity gaps. This article reports findings that are part of a larger qualitative descriptive case study that investigated the implementation and experiences of Eaveston School District’s intentional journey to become a culturally proficient school district. For this article, the authors included findings related to (1) how the implementation of the Cultural Proficiency Framework influenced change, and (2) the challenges educational leaders face while implementing the work of Cultural Proficiency. The findings and conclusions of the study suggest that educators can lead organizational change and increase equity, access, and inclusion for all students by using the Four Tools of Cultural Proficiency to cause shifts from deficit-based to asset-based mindsets about students

Exposure to negative stereotypes influences representations of monetary incentives in the nucleus accumbens

Contemporary society is saturated with negative representations of racial and ethnic minorities. Social science research finds that exposure to such negative stereotypes creates stress above and beyond pre-existing effects of income inequality and structural racism. Neuroscience studies in animals and humans show that life stress modulates brain responses to rewards. However, it is not known whether contending with negative representations of one's social group spills overs to influence reward processing. We used functional magnetic resonance imaging to examine the effects of stigmatizing negative stereotypes on neural responding to the anticipation and consumption of monetary gains and losses in a Mexican American sample. Machine learning analyses indicated that incentive-related patterns of brain activity within the nucleus accumbens differed between Mexican Americans subjected to negative stereotypes and those who were not. This effect occurred for anticipating both gains and losses. Our work suggests that rhetoric stigmatizing Latinos and other minorities could alter how members of such groups process incentives in their environment. These findings contribute to our understanding of the linkage between stigmatizing experiences and motivated behavior, with implications for well-being and health

Neural Correlates of the False Consensus Effect:Evidence for Motivated Projection and Regulatory Restraint

The false consensus effect (FCE), the tendency to project our attitudes and opinions on to others, is a pervasive bias in social reasoning with a range of ramifications for individuals and society. Research in social psychology has suggested that numerous factors (anchoring and adjustment, accessibility, motivated projection, etc.) may contribute to the FCE. In this study, we examine the neural correlates of the FCE and provide evidence that motivated projection plays a significant role. Activity in reward regions (ventromedial pFC and bilateral nucleus accumbens) during consensus estimation was positively associated with bias, whereas activity in right ventrolateral pFC (implicated in emotion regulation) was inversely associated with bias. Activity in reward and regulatory regions accounted for half of the total variation in consensus bias across participants (R2 = .503). This research complements models of the FCE in social psychology, providing a glimpse into the neural mechanisms underlying this important phenomenon

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized geometries of 7211 organic molecules of up to seven heavy atoms, both implementations reach chemical accuracy (1 kcal/mol error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time.Comment: 31 pages, 10 figures, with an S

Defining Cable Broadband Internet Access Service: Background and Analysis of the Supreme Court's Brand X Decision

This report provides an overview of the regulatory actions leading up to and an analysis of the Supreme Court’s decision in National Cable & Telecommunications Association v. Brand X Internet Services. It also provides a discussion of the possible legal and economic implications of the Court’s decision

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized (350 K) geometries of 7211 organic molecules of up to seven heavy atoms (QM7b-T), both RC/LR/RFC and RC/GPR/RFC reach chemical accuracy (1 kcal/mol prediction error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time

Regression-clustering for Improved Accuracy and Training Cost with Molecular-Orbital-Based Machine Learning

Machine learning (ML) in the representation of molecular-orbital-based (MOB) features has been shown to be an accurate and transferable approach to the prediction of post-Hartree-Fock correlation energies. Previous applications of MOB-ML employed Gaussian Process Regression (GPR), which provides good prediction accuracy with small training sets; however, the cost of GPR training scales cubically with the amount of data and becomes a computational bottleneck for large training sets. In the current work, we address this problem by introducing a clustering/regression/classification implementation of MOB-ML. In a first step, regression clustering (RC) is used to partition the training data to best fit an ensemble of linear regression (LR) models; in a second step, each cluster is regressed independently, using either LR or GPR; and in a third step, a random forest classifier (RFC) is trained for the prediction of cluster assignments based on MOB feature values. Upon inspection, RC is found to recapitulate chemically intuitive groupings of the frontier molecular orbitals, and the combined RC/LR/RFC and RC/GPR/RFC implementations of MOB-ML are found to provide good prediction accuracy with greatly reduced wall-clock training times. For a dataset of thermalized (350 K) geometries of 7211 organic molecules of up to seven heavy atoms (QM7b-T), both RC/LR/RFC and RC/GPR/RFC reach chemical accuracy (1 kcal/mol prediction error) with only 300 training molecules, while providing 35000-fold and 4500-fold reductions in the wall-clock training time, respectively, compared to MOB-ML without clustering. The resulting models are also demonstrated to retain transferability for the prediction of large-molecule energies with only small-molecule training data. Finally, it is shown that capping the number of training datapoints per cluster leads to further improvements in prediction accuracy with negligible increases in wall-clock training time

Obesity as Assessed by Body Adiposity Index and Multivariable Cardiovascular Disease Risk

To assess the role of body adiposity index (BAI) in predicting cardiovascular disease (CVD) and coronary heart disease (CHD) mortality, in comparison with body mass index (BMI), waist circumference (WC), and the waist circumference to hip circumference ratio (WHR). This study was a prospective 15 year mortality follow-up of 4175 Australian males, free of heart disease, diabetes and stroke. The Framingham Risk Scores (FRS) for CHD and CVD death were calculated at baseline for all subjects. Multivariable logistic regression was used to assess the effects of the measures of obesity on CVD and CHD mortality, before adjustment and after adjustment for FRS. The predictive ability of BAI, though present in the unadjusted analyses, was generally not significant after adjustment for age and FRS for both CVD and CHD mortality. BMI behaved similarly to BAI in that its predictive ability was generally not significant after adjustments. Both WC and WHR were significant predictors of CVD and CHD mortality and remained significant after adjustment for covariates. BAI appeared to be of potential interest as a measure of % body fat and of obesity, but was ineffective in predicting CVD and CHD