An introduction to time-resolved decoding analysis for M/EEG

The human brain is constantly processing and integrating information in order to make decisions and interact with the world, for tasks from recognizing a familiar face to playing a game of tennis. These complex cognitive processes require communication between large populations of neurons. The non-invasive neuroimaging methods of electroencephalography (EEG) and magnetoencephalography (MEG) provide population measures of neural activity with millisecond precision that allow us to study the temporal dynamics of cognitive processes. However, multi-sensor M/EEG data is inherently high dimensional, making it difficult to parse important signal from noise. Multivariate pattern analysis (MVPA) or "decoding" methods offer vast potential for understanding high-dimensional M/EEG neural data. MVPA can be used to distinguish between different conditions and map the time courses of various neural processes, from basic sensory processing to high-level cognitive processes. In this chapter, we discuss the practical aspects of performing decoding analyses on M/EEG data as well as the limitations of the method, and then we discuss some applications for understanding representational dynamics in the human brain

Alien Registration- Thomas, Amanda (Fort Kent, Aroostook County)

https://digitalmaine.com/alien_docs/36328/thumbnail.jp

Development and Validation of a Multidimensional Political Behavior Scale

Years of research conducted into organizational politics has resulted in an expanded understanding of what politics “do” through the investigation of antecedents and outcomes (Lepisto & Pratt, 2012). The literature is somewhat deficient, however, in explaining and measuring what politics “are”. While there are numerous existing measures of organizational politics, the measurement and methodology in this area remains complex due to several issues. The existing literature notes design and measurement (Ferris, Adams, Kolodinsky, Hochwarter, & Ammeter, 2002; Nye & Witt, 1993), definitional (Gunn & Chen, 2006; Lepisto & Pratt, 2012), and level of analysis (Dipboye & Foster, 2006; Fedor & Maslyn, 2002) issues. This research expands the existing literature by identifying areas for improvement within the organizational politics field. Through three studies new items were created and a combination of new and existing items were reviewed and narrowed to create a twenty-six-item, behaviorally based measure of organizational politics. Analyses were conducted to establish and validate the factor structure of the new measure and nomological network relationships were reviewed. Findings show the final measure relates to known correlates of organizational politics as expected, while also providing an opportunity to examine known relationships more broadly at the dimension level due to the expanded construct coverage

Where is the evidence: realising the value of grey literature for public policy and practice

This paper discusses the ways in which the internet has profoundly changed how we produce, use and collect research and information for public policy and practice, particularly focusing on the benefits and challenges presented by grey literature. The authors argue that grey literature (i.e. material produced and published by organisations  without recourse to the commercial or scholarly publishing industry) is a key part of the evidence produced and used for public policy and practice. Through surveys of users, producing organisations and collecting services a detailed picture is provided of the role, importance and economic value of grey literature. However, finding and accessing policy information is a time-consuming task made harder by poor production and management of resources and a lack of large-scale collection services able to host and make available  relevant, high-quality resources quickly and efficiently. The paper makes recommendations for changes that would maximise the benefits of grey literature in the public interest and seeks feedback from readers to inform the final report of the research project. Public policy work increasingly relies on a wide range of resources — some are traditional scholarly publications, but the majority are ‘grey literature’. Reports, discussion papers, briefings, reviews and data sets produced by government, academic centres, NGOs, think tanks and companies are heavily used and highly valued in policy and practice work, forming a key part of the evidence base. The huge amount of information and research published online provides unprecedented access to knowledge, from a wide range of sources, enabling a much greater level of understanding and participation in public interest issues. It also brings a number of challenges: searching, sifting, evaluating and accessing information and research are time-consuming and often frustrating tasks occupying a large portion of the work hours of those engaged in policy work. Online publishing also creates a new paradigm for those whose task it is to support policy and practice work through effective resource provision and information management. As a result, digital curation of policy resources, particularly grey literature, is dispersed and fragmented, creating a digital black hole of resources that are lost from online access over time. The aim of the Grey Literature Strategies research project is to investigate grey literature’s role and importance in policy work and find ways to enhance its value. A key method used was online surveys of producers, users, and collectors of information and research for policy and practice, conducted during 2013

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management