Emergent modular neural control drives coordinated motor actions.

A remarkable feature of motor control is the ability to coordinate movements across distinct body parts into a consistent, skilled action. To reach and grasp an object, 'gross' arm and 'fine' dexterous movements must be coordinated as a single action. How the nervous system achieves this coordination is currently unknown. One possibility is that, with training, gross and fine movements are co-optimized to produce a coordinated action; alternatively, gross and fine movements may be modularly refined to function together. To address this question, we recorded neural activity in the primary motor cortex and dorsolateral striatum during reach-to-grasp skill learning in rats. During learning, the refinement of fine and gross movements was behaviorally and neurally dissociable. Furthermore, inactivation of the primary motor cortex and dorsolateral striatum had distinct effects on skilled fine and gross movements. Our results indicate that skilled movement coordination is achieved through emergent modular neural control

How has the macroeconomic imbalances procedure worked in practice to improve the resilience of the euro area? March 24 2020

This paper shows how the Macroeconomic Imbalances Procedure (MIP) could be streamlined and its underlying conceptual framework clarified. Implementation of the country-specific recommendations is low; their internal consistency is sometimes missing; despite past reforms, the MIP remains largely a countryby-country approach running the risk of aggravating the deflationary bias in the euro area. We recommend to streamline the scoreboard around a few meaningful indicators, involve national macro-prudential and productivity councils, better connect the various recommendations, simplify the language and further involve the Commission into national policy discussions. This document was prepared for the Economic Governance Support Unit at the request of the ECON Committee

The effects of entrepreneurship education

Entrepreneurship education ranks high on policy agendas in Europe and the US, but little research is available to assess its impact. To help close this gap we investigate whether entrepreneurship education a?ects intentions to be entrepreneurial uniformly or whether it leads to greater sorting of students. The latter can reduce the average intention to be entrepreneurial and yet be socially beneficial. This paper provides a model of learning in which entrepreneurship education generates signals to students. Drawing on the signals, students evaluate their aptitude for entrepreneurial tasks. The model is tested using data from a compulsory entrepreneurship course. Using ex ante and ex post survey responses from students, we find that intentions to found decline somewhat although the course has significant positive e?ects on students’ self-assessed entrepreneurial skills. The empirical analysis supports the hypothesis that students receive informative signals and learn about their entrepreneurial aptitude. We outline implications for educators and public policy

Digital image processing of the Ghent altarpiece : supporting the painting's study and conservation treatment

In this article, we show progress in certain image processing techniques that can support the physical restoration of the painting, its art-historical analysis, or both. We show how analysis of the crack patterns could indicate possible areas of overpaint, which may be of great value for the physical restoration campaign, after further validation. Next, we explore how digital image inpainting can serve as a simulation for the restoration of paint losses. Finally, we explore how the statistical analysis of the relatively simple and frequently recurring objects (such as pearls in this masterpiece) may characterize the consistency of the painter’s style and thereby aid both art-historical interpretation and physical restoration campaign

The Validation of Speech Corpora

1.2 Intended audience........................

A Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics

Numerous statistics have been proposed for the measure of offensive ability in major league baseball. While some of these measures may offer moderate predictive power in certain situations, it is unclear which simple offensive metrics are the most reliable or consistent. We address this issue with a Bayesian hierarchical model for variable selection to capture which offensive metrics are most predictive within players across time. Our sophisticated methodology allows for full estimation of the posterior distributions for our parameters and automatically adjusts for multiple testing, providing a distinct advantage over alternative approaches. We implement our model on a set of 50 different offensive metrics and discuss our results in the context of comparison to other variable selection techniques. We find that 33/50 metrics demonstrate signal. However, these metrics are highly correlated with one another and related to traditional notions of performance (e.g., plate discipline, power, and ability to make contact)

Disengagement of motor cortex from movement control during long-term learning.

Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training. We found that M1 inactivation impaired the forelimb movements in the early and middle stages, but not in the late stage, indicating that the movements that initially required M1 became independent of M1. As previously shown, M1 population activity became more consistent across trials from the early to middle stage while task performance rapidly improved. However, from the middle to late stage, M1 population activity became again variable despite consistent expert behaviors. This later decline in activity consistency suggests dissociation between M1 and movements. These findings suggest that long-term motor learning can disengage M1 from movement control