Improving understanding of EEG measurements using transparent machine learning models

Physiological datasets such as Electroencephalography (EEG) data offer an insight into some of the less well understood aspects of human physiology. This paper investigates simple methods to develop models of high level behavior from low level electrode readings. These methods include using neuron activity based pruning and large time slices of the data. Both approaches lead to solutions whose performance and transparency are superior to existing methods

Utah Science Vol. 63 No. 1, 2006

2 Copy CATTLE 8 BACTERIA: THE BACKBONE OF FERMENTATION 14 GREEN GENES 36 TEACHINC CHILDREN TO FICHT BAC! 26 2006 projects & Financial Report 17 SEEDS: New people, grants and contracts in science 19 SYNTHESIS: Science at Utah State 40 SEEK: Students in science 41 SEARCH: Science on the we

VCU Campus Tree Plan

Throughout the past several years VCU has grown tremendously as a large public research university and an anchor institution within the city of Richmond However, VCU also has been working towards a more sustainable future and is committed to furthering sustainability in its community and campuses. While VCU has several long-range plans dedicated towards different aspects of the University’s planning and design initiatives, the University does not have any plan dedicated specifically towards its campus trees or tree canopy. An analysis of the existing conditions on VCU’s campuses found that there is low amount of urban tree canopy cover, high amount of impervious surface areas as well as a lack of policies in place relating to oversight and administration for campus trees. Additionally, VCU’s urban tree canopy (UTC) faces threats posed by climate change and potential shrinkage of its tree canopy due to future increased development. The VCU Campus Tree Plan aims to provide the University with the framework and policy guidelines for the preservation, maintenance and planting of the urban tree UTC on its campuses. The overall goal of the VCU Campus Tree Plan is to provide a safe, healthy, attractive and sustainable campus forest. This will help to enhance the VCU campuses in terms of aesthetic appearance, energy efficiency and the social/educational interactions that take place through the wide range of benefits that urban trees provide. The implementation of this plan will provide the University with many tangible benefits such as a reduction in the urban heat island effect that VCU’s campuses experience, mitigation of stormwater runoff during heavy precipitation events, increased physical and psychological well-being for residents, increased revenue for business owners and business districts and increased aesthetic appearances for neighborhoods and retail areas. All of these benefits will help enhance the overall environment for VCU’s students and faculty and increase VCU’s standing as a premier urban university. The success of this plan will primarily depend on the amount of implementation carried out by the VCU Offices of Sustainability, Planning & Design and Facilities Management. It is hoped that within the next couple decades VCU will have established itself as a well treed urban university

Adolescent coping styles and response to stress: A study of the relationship between the preferred coping styles of female senior high school students and their levels of anxiety and self-confidence when facing a major academic stressor

A growing body of research indicates the importance of coping strategies when an individual responds to environmental demands. Community concern about the maladaptive responses of some adolescents, limited research with this age group, and the development of a new Australian measure of adolescent coping provided the impetus for this study. The study was conducted with 141 female students in their final year of High School. They completed the Adolescent Coping Scale (ACS) in March, and measures of anxiety and self-confidence in November, just before major external examinations. Behavioural rating scales were completed by parents and teachers. The adolescent group reported frequent use of coping strategies which research indicates are likely to be effective, and relatively little use of ineffective strategies. When facing a severe academic stressor, they were self-confident but reported very high levels of anxiety, which was cognitive rather than somatic in focus. The few students whose ACS scores showed relatively high use of ineffective and low use of effective coping strategies were identified as At risk . When compared with a contrasting sub-group, the At-risk students were significantly more anxious and less self-confident. There was no evidence that parents or teachers were aware of the adolescents\u27 high levels of anxiety. The findings provide support for the predictive validity of the ACS, and have implications for helping adolescents cope with stress and developmental demands. Further research directions are suggested

Preacher\u27s Magazine Volume 52 Number 04

The New Day Dawned, Editorial On the Inductive Method of Bible Study, Ross E. Price Helping the Backslider, Lyle Pointer Reflections, C. T. Corbett Have Faith in a Failure, Ross W. Hayslip Wesley’s Views on Humility, George E. Failing Energy That Boils Over, Gene Van Note The High Point of the Year, Practical Points Two Roads to Canaan, Ralph A. Gallagher Productivity and the Pulpit, Jerald L. Duff Praise Your Way Through, Audrey Williamson The Starting Point, C. Neil Strait Seeds for Sermons, Mark E. Moore Gleanings from the Greek, Ralph Earle The Meaning of Easter (sermon), James F. Spruill DEPARTMENTS Wesleyana The Preacher’s Wife In the Study Timely Outlines Bulletin Barrel Here and There Among Books Preachers’ Exchange Among Ourselveshttps://digitalcommons.olivet.edu/cotn_pm/1597/thumbnail.jp

Conceptualising neuroscience-based leadership behaviour

This thesis primarily focuses on conceptualising Neuroscience Based Leadership (NSBL) by providing a working definition of NSBL, describing the foundational concepts and core behaviours of neuroscience-based leadership (NSBL), and presenting a conceptual framework that integrates interdisciplinary perspectives on leadership behaviour. This was achieved by: 1. Reviewing existing relevant scientific literature and highlighting current knowledge gaps in the conceptualisations of NSBL using Leadership Behaviour, Social Cognitive Neuroscience (SCN), and Neuropsychotherapy (NP) 2. Conducting a small-scale research project using semi-structured, in-depth interviews with three neuroscientists who have employed neuroscience-based diagnostics in leadership development within a corporate context. This study’s key findings reveal key conceptual themes with the following theoretical propositions that underpin NSBL key behaviours: social safety is a primary operating principle; conscious thinking and nonconscious processes drive behaviour; nature-nurture dynamics influence behaviour; experienced-based neuroplasticity drives change; and overlapping large-scale brain networks enable information processing in the brain. 3. Designing and implementing a qualitative Delphi study involving 33 experienced professionals in NSBL to explore how NSBL is defined, conceptualise NSBL as a different domain of leadership behaviour, and provide descriptors of NSBL key behaviours 4. Adopting a case study approach involving an organisational psychologist experienced in Neuropsychotherapy and drawing on his views and experiences to produce a single-case study of NSBL within the context of organisational psychology and applied organisational neuroscience (AONS). 5. Undertaking a reflective and critical review of the four pieces of research and proposing a theoretical framework of NSBL, specifically within formal organisations, to inform, support, foster and develop future NSBL-based behaviour. The contribution of this study is broad in that it offers a working definition of neuroscience-based leadership and an interdisciplinary conceptual framework to guide practitioners and further research. This conceptual framework integrates theoretical propositions regarding leadership behaviour from Leadership Behaviour theory, Social Cognitive and Affective Neuroscience, and Neuropsychotherapy. The theoretical framework of NSBL addresses gaps in the literature by differentiating four domains of NSBL: stress resilience-focused core behaviours, affect and emotional-focused core behaviours, relationship-focused core behaviours, and task-focused core behaviours. It also provides neuroscientific concepts that underpin behaviour. The contribution to practice is that this study advances the understanding of how formal organisations can apply a neuroscientific lens to inform the design of leadership development interventions. This integrative, interdisciplinary theoretical framework can be used for leadership coaching at an individual level. At the group level, it can facilitate team building. It can provide a neuroscientific language for mental experience at an organisational level, thereby enhancing the explanatory power of concepts in leadership and organisational behaviour

1933-1934 Undergraduate Catalog

1933-1934 undergraduate catalog of Morehead State Teachers College

1951-1953 Undergraduate Catalog

1951-1953 undergraduate catalog of Morehead State College

Objective measures of complexity

Mesures Objectives de la Complexité pour la Prise de Décision Dynamique. La gestion efficace de systèmes sociotechniques complexes dépend d’une compréhension des interrelations dynamiques entre les composantes de ces systèmes, de leur évolution à travers le temps, ainsi que du degré d’incertitude auquel les décideurs sont exposés. Quelles sont les caractéristiques de la prise de décision complexe qui ont un impact sur la performance humaine dans l’environnement moderne du travail, constamment en fluctuation et sous la pression du temps, exerçant de lourdes demandes sur la cognition ? La prise de décision complexe est un concept issu de la macrocognition, impliquant des processus et des fonctions de bas et haut niveaux de description tels que la métacognition, soit pour un individu de penser à propos de son propre processus de pensées. Dans le cas particulier de la prise de décision complexe, ce phénomène est nommé la pensée systémique. L’étude de la prise de décision complexe en dehors de l’environnement traditionnel du laboratoire, permettant un haut niveau de contrôle mais un faible degré de réalisme, est malheureusement difficile et presque impossible. Une méthode de recherche plus appropriée pour la macrocognition est l’expérimentation basée sur la simulation, à l’aide de micromondes numérisés sous la forme de jeux sérieux. Ce paradigme de recherche est nommé la prise de décision dynamique (PDD), en ce qu’il tient compte des caractéristiques de problèmes de prise de décision complexe telles que des séquences complexes de décisions et de changements d’états d’un problème interdépendants, qui peuvent changer de façon spontanée ou comme conséquence de décisions préalables, et pour lesquels la connaissance et la compréhension du décideur peut n’être que partielle ou incertaine. Malgré la quantité de recherche concernant la PDD à propos des difficultés encourues pour la performance humaine face à des problèmes de prise de décision complexe, l’acquisition de connaissances à propos de systèmes complexes, et à savoir si le transfert de l’apprentissage est possible, il n’existe pas de mesure quantitative de ce en quoi un problème de décision est considéré comme étant complexe. La littérature scientifique mentionne des éléments qualitatifs concernant les systèmes complexes (tels que des interrelations dynamiques, une évolution non-linéaire d’un système à travers le temps, et l’incertitude à propos des états d’un système et des issues des décisions), mais des mesures quantitatives et objectives exprimant la complexité de problèmes de décision n’ont pas été développées. Cette dissertation doctorale présente les concepts, la méthodologie et les résultats impliqués dans un projet de recherche visant à développer des mesures objectives de la complexité basées sur les caractéristiques de problèmes de prise de décision dynamique pouvant expliquer et prédire la performance humaine. En s’inspirant de divers domaines d’application de la théorie de la complexité tels que la complexité computationnelle, la complexité systémique, et l’informatique cognitive, un modèle formel des paramètre de la complexité pour des tâches de prise de décision dynamique a été élaboré. Un ensemble de dix mesures objectives de la complexité a été développé, consistant en des mesures de la complexité structurelle, des mesures de la complexité informationnelle, la complexité de la charge cognitive, et des mesures de la difficulté d’un problème, de la non-linéarité des relations, de l’incertitude concernant l’information et les décisions, ainsi qu’une mesure de l’instabilité d’un système dynamique sous des conditions d’inertie. Une analyse des résultats expérimentaux colligés à partir de cinq scénarios de PDD révèle qu’un nombre restreint de candidats parmi des modèles de régression linéaires multiple permet d’expliquer et de prédire les résultats de performance humaine, mais au prix de certaines violations des postulats de l’approche classique de la régression linéaire. De plus, ces mesures objectives de la complexité présentent un degré élevé de multicolinéarité, causée d’une part par l’inclusion de caractéristiques redondantes dans les calculs, et d’autre part par une colinéarité accidentelle imputable à la conception des scénarios de PDD. En tenant compte de ces deux considérations ainsi que de la variance élevée observée dans les processus macrocognitifs impliqués dans la prise de décision complexe, ces modèles présentent des valeurs élevées pour le terme d’erreur exprimant l’écart entre les observations et les prédictions des modèles. Une analyse additionnelle explore l’utilisation de méthodes alternatives de modélisation par régression afin de mieux comprendre la relation entre les paramètres de la complexité et les données portant sur performance humaine. Nous avons d’abord opté pour une approche de régression robuste afin d’augmenter l’efficience de l’analyse de régression en utilisant une méthode réduisant la sensibilité des modèles de régression aux observations influentes. Une seconde analyse élimine la source de variance imputable aux différences individuelles en focalisant exclusivement sur les effets imputables aux conditions expérimentales. Une dernière analyse utilise des modèles non-linéaires et non-paramétriques afin de pallier les postulats de la modélisation par régression, à l’aide de méthodes d’apprentissage automatique (machine learning). Les résultats suggèrent que l’approche de régression robuste produit des termes d’erreur substantiellement plus faibles, en combinaison avec des valeurs élevées pour les mesures de variance expliquée dans les données de la performance humaine. Bien que les méthodes non-linéaires et non-paramétriques produisent des modèles marginalement plus efficients en comparaison aux modèles de régression linéaire, la combinaison de ces modèles issus du domaine de l’apprentissage automatique avec les données restreintes aux effets imputables aux conditions expérimentales produit les meilleurs résultats relativement à l’ensemble de l’effort de modélisation et d’analyse de régression. Une dernière section présente un programme de recherche conçu pour explorer l’espace des paramètres pour les mesures objectives de la complexité avec plus d’ampleur et de profondeur, afin d’appréhender les combinaisons des caractéristiques des problèmes de prise de décision complexe qui sont des facteurs déterminants de la performance humaine. Les discussions concernant l’approche expérimentale pour la PDD, les résultats de l’expérimentation relativement aux modèles de régression, ainsi qu’à propos de l’investigation de méthodes alternatives visant à réduire la composante de variance menant à la disparité entre les observations et les prédictions des modèles suggèrent toutes que le développement de mesures objectives de la complexité pour la performance humaine dans des scénarios de prise de décision dynamique est une approche viable à l’approfondissement de nos connaissances concernant la compréhension et le contrôle exercés par un être humain face à des problèmes de décision complexe.Objective Measures of Complexity for Dynamic Decision-Making. Managing complex sociotechnical systems depends on an understanding of the dynamic interrelations of such systems’ components, their evolution over time, and the degree of uncertainty to which decision makers are exposed. What features of complex decision-making impact human performance in the cognitively demanding, ever-changing and time pressured modern workplaces? Complex decision-making is a macrocognitive construct, involving low to high cognitive processes and functions, such as metacognition, or thinking about one’s own thought processes. In the particular case of complex decision-making, this is called systems thinking. The study of complex decision-making outside of the controlled, albeit lacking in realism, traditional laboratory environment is difficult if not impossible. Macrocognition is best studied through simulation-based experimentation, using computerized microworlds in the form of serious games. That research paradigm is called dynamic decision-making (DDM), as it takes into account the features of complex decision problems, such as complex sequences of interdependent decisions and changes in problem states, which may change spontaneously or as a consequence of earlier decisions, and for which the knowledge and understanding may be only partial or uncertain. For all the research in DDM concerning the pitfalls of human performance in complex decision problems, the acquisition of knowledge about complex systems, and whether a learning transfer is possible, there is no quantitative measure of what constitutes a complex decision problem. The research literature mentions the qualities of complex systems (a system’s dynamical relationships, the nonlinear evolution of the system over time, and the uncertainty about the system states and decision outcomes), but objective quantitative measures to express the complexity of decision problems have not been developed. This dissertation presents the concepts, methodology, and results involved in a research endeavor to develop objective measures of complexity based on characteristics of dynamic decision-making problems which can explain and predict human performance. Drawing on the diverse fields of application of complexity theory such as computational complexity, systemic complexity, and cognitive informatics, a formal model of the parameters of complexity for dynamic decision-making tasks has been elaborated. A set of ten objective measures of complexity were developed, ranging from structural complexity measures, measures of information complexity, the cognitive weight complexity, and measures of problem difficulty, nonlinearity among relationships, information and decision uncertainty, as well as a measure of the dynamical system’s instability under inertial conditions. An analysis of the experimental results gathered using five DDM scenarios revealed that a small set of candidate models of multiple linear regression could explain and predict human performance scores, but at the cost of some violations of the assumptions of classical linear regression. Additionally, the objective measures of complexity exhibited a high level of multicollinearity, some of which were caused by redundant feature computation while others were accidentally collinear due to the design of the DDM scenarios. Based on the aforementioned constraints, and due to the high variance observed in the macrocognitive processes of complex decision-making, the models exhibited high values of error in the discrepancy between the observations and the model predictions. Another exploratory analysis focused on the use of alternative means of regression modeling to better understand the relationship between the parameters of complexity and the human performance data. We first opted for a robust regression analysis to increase the efficiency of the regression models, using a method to reduce the sensitivity of candidate regression models to influential observations. A second analysis eliminated the within-treatment source of variance in order to focus exclusively on between-treatment effects. A final analysis used nonlinear and non-parametric models to relax the regression modeling assumptions, using machine learning methods. It was found that the robust regression approach produced substantially lower error values, combined with high measures of the variance explained for the human performance data. While the machine learning methods produced marginally more efficient models of regression for the same candidate models of objective measures of complexity, the combination of the nonlinear and non-parametric methods with the restricted between-treatment dataset yielded the best results of all of the modeling and analyses endeavors. A final section presents a research program designed to explore the parameter space of objective measures of complexity in more breadth and depth, so as to weight which combinations of the characteristics of complex decision problems are determinant factors on human performance. The discussions about the experimental approach to DDM, the experimental results relative to the regression models, and the investigation of further means to reduce the variance component underlying the discrepancy between the observations and the model predictions all suggest that establishing objective measures of complexity for human performance in dynamic decision-making scenarios is a viable approach to furthering our understanding of a decision maker’s comprehension and control of complex decision problems