Neural correlates of intentional and stimulus-driven inhibition: a comparison

People can inhibit an action because of an instruction by an external stimulus, or because of their own internal decision. The similarities and differences between these two forms of inhibition are not well understood. Therefore, in the present study the neural correlates of intentional and stimulus-driven inhibition were tested in the same subjects. Participants performed two inhibition tasks while lying in the scanner: the marble task in which they had to choose for themselves between intentionally acting on, or inhibiting a prepotent response to measure intentional inhibition, and the classical stop signal task in which an external signal triggered the inhibition process. Results showed that intentional inhibition decision processes rely on a neural network that has been documented extensively for stimulus-driven inhibition, including bilateral parietal and lateral prefrontal cortex and pre-supplementary motor area. We also found activation in dorsal frontomedian cortex and left inferior frontal gyrus during intentional inhibition that depended on the history of previous choices. Together, these results indicate that intentional inhibition and stimulus-driven inhibition engage a common inhibition network, but intentional inhibition is also characterized by additional context-dependent neural activation in medial prefrontal cortex

Core measures of inflation as predictors of total inflation

Two rationales offered for policymakers' focus on core measures of inflation as a guide to underlying inflation are that core inflation omits food and energy prices, which are thought to be more volatile than other components, and that core inflation is thought to be a better predictor of total inflation over time horizons of import to policymakers. The authors' investigation finds little support for either rationale. They find that food and energy prices are not the most volatile components of inflation and that depending on which inflation measure is used, core inflation is not necessarily the best predictor of total inflation. However, they do find that combining CPI and PCE inflation measures can lead to statistically significant more accurate forecasts of each inflation measure, suggesting that each measure includes independent information that can be exploited to yield better forecasts.Inflation (Finance)

Core measures of inflation as predictors of total inflation

Policymakers tend to focus on core inflation measures because they are thought to be better predictors of total inflation over time horizons of import to policymakers. The authors find little support for this assumption. While some measures of core inflation are less volatile than total inflation, core inflation is not necessarily the best predictor of total inflation. The relative forecasting performance of models using core inflation and those using only total inflation depends on the inflation measure and time horizon of the forecast. Unlike previous studies, the authors provide a measure of the statistical significance of the difference in forecast errors. ; Supersedes Working Paper 08-9.Inflation (Finance)

Automated data pre-processing via meta-learning

The final publication is available at link.springer.comA data mining algorithm may perform differently on datasets with different characteristics, e.g., it might perform better on a dataset with continuous attributes rather than with categorical attributes, or the other way around. As a matter of fact, a dataset usually needs to be pre-processed. Taking into account all the possible pre-processing operators, there exists a staggeringly large number of alternatives and nonexperienced users become overwhelmed. We show that this problem can be addressed by an automated approach, leveraging ideas from metalearning. Specifically, we consider a wide range of data pre-processing techniques and a set of data mining algorithms. For each data mining algorithm and selected dataset, we are able to predict the transformations that improve the result of the algorithm on the respective dataset. Our approach will help non-expert users to more effectively identify the transformations appropriate to their applications, and hence to achieve improved results.Peer ReviewedPostprint (published version

Early detection of children at risk for antisocial behaviour using data from routine preventive child healthcare

BACKGROUND: Youth antisocial behaviour is highly prevalent. Young people are usually not willing to disclose such behaviour to professionals and parents. Our aim was to assess whether child health professionals (CHP) working in preventive child healthcare could identify pre-adolescents at risk for antisocial behaviour through using data that they obtain in routine practice. METHODS: CHPs examined a national sample of 974 pre-adolescents aged 8-12 years (response 79.1%), and interviewed parents and children during routine well-child assessments. We obtained data on family background and current health of the child from the CHP; on developmental concerns from parents, and on social and emotional well-being, injuries, and substance use from the children. Antisocial behaviour concerned the adolescent-reported 15 item International Self-Reported Delinquency study questionnaire, among which are 5 items on violence against people. RESULTS: The prevalence of 2+acts of any antisocial behaviour was 21.8%, and 33.9% for 1+acts of violence (10.5% for 2+). Children who were male, had a young mother, no parent employed, recent injuries, poor performance at school or who were bored by school, and who had parental concerns more often reported 2+antisocial acts and 1+violence against people. Detection algorithms on the basis of these variables were moderately able to classify outcomes, with Areas-Under-the-Curves ranging from 0.66 to 0.71. CONCLUSIONS: Data from routine well-child assessment can help CHPs to detect pre-adolescents at risk for antisocial behaviour, but detection algorithms need to be further improved. This could be done by obtaining additional information on factors that are associated with antisocial behaviour

Patients' perceptions of a NHS Health Check in the primary care setting

The NHS Health Checks is a cardiovascular disease (CVD) risk assessment and management programme for individuals in England aged between 40 and 74 with the aim of identifying previously unassessed individuals that are at high risk of CVD. Little research to date has explored patient perceptions and opinions of Health Checks. This paper aims to investigate the perceptions and opinions of patients who had attended a Health Check appointment within a cohort of 83 General Practices in Gloucestershire. A cross sectional survey of patients who had completed a Health Check appointment during the period May to June 2012 within a single county in England. Quantitative and qualitative data were acquired from 1,011 standardised and anonymised patient surveys sent out by a Health Check Commissioner and GPs. Data gathered included perceptions concerning all aspects of the Health Checks process and actual appointment. Descriptive analysis was used to interrogate the quantitative data. Inductive content analysis was used to analyse qualitative data. Concerns about health were a principal driver of attendance. Reassurance, access to health information and guidance, and the identification of CVD risk and CVD diagnosis were perceived as key benefits of attending the appointment. Principal disadvantages included inconsistencies in the Health Check process, administration of appointments and a lack of appropriate follow up advice. Health Checks are popular with patients and provide useful outcomes but greater consistency is needed in engaging patients and describing its purpose

Simulation studies of improved sounding systems

Two instrument designs for indirect satellite sounding of the atmosphere in the infrared are represented by the High Resolution Infra-Red Sounder, Model 2 (HIRS-2) and by the Advanced Meteorological Temperature Sounder (AMTS). The relative capabilities of the two instruments were tested by simulating satellite measurements from a group of temperature soundings, allowing the two participants to retrieve the temperature profiles from the simulated data, and comparing the results with the original temperature profiles. Four data sets were produced from radiosondes data extrapolated to a suitable altitude, representing continents and oceans, between 30S and 30N. From the information available, temperature profiles were retrieved by two different methods, statistical regression and inversion of the radiative transfer equation. Results show the consequence of greater spectral purity, concomitant increase in the number of spectral intervals, and the better spatial resolution in partly clouded areas. At the same time, the limitation of the HIRS-2 without its companion instrument leads to some results which should be ignored in comparing the two instruments. A clear superiority of AMTS results is shown