Path Dependence in Risky Choice: Affective and Deliberative Processes in Brain and Behavior
tDecision-makers show an increased risk appetite when they gamble with previously wonmoney, the house money effect, and when they have a chance to make up for a prior loss,the break even effect. To explore the origins of these effects, we use functional magneticresonance imaging to record the brain activities of subjects while they make sequential riskychoices. The behavioral data from our experiment confirm the path dependence of choices,despite the short trial duration and the many task repetitions required for neuroimaging.The brain data yield evidence that the increased risk appetite after gains and losses is relatedto an increased activity of affective brain processes and a decreased activity of deliberativebrain processes.
Recent years have witnessed a massive push towards reproducible research in neuroscience. Unfortunately, this endeavor is often challenged by the large diversity of tools used, project-specific custom code and the difficulty to track all user-defined parameters. NeuroPycon is an open-source multi-modal brain data analysis toolkit which provides Python-based template pipelines for advanced multi-processing of MEG, EEG, functional and anatomical MRI data, with a focus on connectivity and graph theoretical analyses. Importantly, it provides shareable parameter files to facilitate replication of all analysis steps. NeuroPycon is based on the NiPype framework which facilitates data analyses by wrapping many commonly-used neuroimaging software tools into a common Python environment. In other words, rather than being a brain imaging software with is own implementation of standard algorithms for brain signal processing, NeuroPycon seamlessly integrates existing packages (coded in python, Matlab or other languages) into a unified python framework. Importantly, thanks to the multi-threaded processing and computational efficiency afforded by NiPype, NeuroPycon provides an easy option for fast parallel processing, which critical when handling large sets of multi-dimensional brain data. Moreover, its flexible design allows users to easily configure analysis pipelines by connecting distinct nodes to each other. Each node can be a Python-wrapped module, a user-defined function or a well-established tool (e.g. MNE-Python for MEG analysis, Radatools for graph theoretical metrics, etc.). Last but not least, the ability to use NeuroPycon parameter files to fully describe any pipeline is an important feature for reproducibility, as they can be shared and used for easy replication by others. The current implementation of NeuroPycon contains two complementary packages: The first, called ephypype, includes pipelines for electrophysiology analysis and a command-line interface for on the fly pipeline creation. Current implementations allow for MEG/EEG data import, pre-processing and cleaning by automatic removal of ocular and cardiac artefacts, in addition to sensor or source-level connectivity analyses. The second package, called graphpype, is designed to investigate functional connectivity via a wide range of graph-theoretical metrics, including modular partitions. The present article describes the philosophy, architecture, and functionalities of the toolkit and provides illustrative examples through interactive notebooks. NeuroPycon is available for download via github (https://github.com/neuropycon) and the two principal packages are documented online (https://neuropycon.github.io/ephypype/index.html, and https://neuropycon.github.io/graphpype/index.html). Future developments include fusion of multi-modal data (eg. MEG and fMRI or intracranial EEG and fMRI). We hope that the release of NeuroPycon will attract many users and new contributors, and facilitate the efforts of our community towards open source tool sharing and development, as well as scientific reproducibility.
Students' internet usage attracts the attention of many researchers in different countries. Differences in internet penetration in diverse countries lead us to ask about the interaction of medium and culture in this process. In this paper we present an analysis based on a sample of 825 students from 18 Russian universities and discuss findings on particularities of students' ICT usage. On the background of the findings of the study, based on data collected in 2008-2009 year during a project "A сross-cultural study of the new learning culture formation in Germany and Russia", we discuss the problem of plagiarism in Russia, the availability of ICT features in Russian universities and an evaluation of the attractiveness of different categories of ICT usage and gender specifics in the use of ICT.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The results of cross-cultural research of implicit theories of innovativeness among students and teachers, representatives of three ethnocultural groups: Russians, the people of the North Caucasus (Chechens and Ingushs) and Tuvinians (N=804) are presented. Intergroup differences in implicit theories of innovativeness are revealed: the ‘individual’ theories of innovativeness prevail among Russians and among the students, the ‘social’ theories of innovativeness are more expressed among respondents from the North Caucasus, Tuva and among the teachers. Using the structural equations modeling the universal model of values impact on implicit theories of innovativeness and attitudes towards innovations is constructed. Values of the Openness to changes and individual theories of innovativeness promote the positive relation to innovations. Results of research have shown that implicit theories of innovativeness differ in different cultures, and values make different impact on the attitudes towards innovations and innovative experience in different cultures.