The purpose of the current research was to examine how a novel balance task is learnt by individuals with a mature neurological system, and to investigate the responses of experienced hand balancers to mechanical and sensory perturbations. Balance in each posture was assessed by various techniques, including: traditional measures of centre of pressure, nonlinear time series analysis of centre of pressure, estimates of feedback time delay from cross correlations and delayed regression models, and calculation of small, medium, and large movement corrections. Data from this study suggests that the best balance metric for distinguishing between each of the balance conditions was the traditional balance measure of sway velocity. However, sway velocity cannot provide any further information on the underlying process of balance. Nonlinear measures of balance offer insight into the underlying deterministic processes that control balance, offering measures of system determinism, complexity, and predictability. Assessments of feedback time delay and movement corrections provide both an insight into the control of posture and help distinguish one condition from another. Both feedback time delay and movement corrections and magnitudes may be used simultaneously to delve further into the control of posture. Delayed regression models seem to be an appropriate and useful tool for estimating feedback time delays during balance. Findings support the use of the third term in the adapted regression model as a means of estimating the effect of passive stiffness on feedback time delay. Generally, with increased duration in handstand subjects displayed reduced sway as measured by traditional measures of balance. A more marked change in nonlinear measures of balance can be seen, with quicker reductions in variance for some nonlinear measures of balance than in the traditional measures. It may be that more pronounced changes in nonlinear measures represent changes in the subjects underlying process of postural control, whereas less pronounced changes in traditional measures relate more to their general ability or performance in the balance task.

The motor control of anthropomorphic robotic systems is a challenging computational task mainly because of the high levels of redundancies such systems exhibit. Optimality principles provide a general strategy to resolve such redundancies in a task driven fashion. In particular closed loop optimisation, i.e., optimal feedback control (OFC), has served as a successful motor control model as it unifies important concepts such as costs, noise, sensory feedback and internal models into a coherent mathematical framework. Realising OFC on realistic anthropomorphic systems however is non-trivial: Firstly, such systems have typically large dimensionality and nonlinear dynamics, in which case the optimisation problem becomes computationally intractable. Approximative methods, like the iterative linear quadratic gaussian (ILQG), have been proposed to avoid this, however the transfer of solutions from idealised simulations to real hardware systems has proved to be challenging. Secondly, OFC relies on an accurate description of the system dynamics, which for many realistic control systems may be unknown, difficult to estimate, or subject to frequent systematic changes. Thirdly, many (especially biologically inspired) systems suffer from significant state or control dependent sources of noise, which are difficult to model in a generally valid fashion. This thesis addresses these issues with the aim to realise efficient OFC for anthropomorphic manipulators. First we investigate the implementation of OFC laws on anthropomorphic hardware. Using ILQG we optimally control a high-dimensional anthropomorphic manipulator without having to specify an explicit inverse kinematics, inverse dynamics or feedback control law. We achieve this by introducing a novel cost function that accounts for the physical constraints of the robot and a dynamics formulation that resolves discontinuities in the dynamics. The experimental hardware results reveal the benefits of OFC over traditional (open loop) optimal controllers in terms of energy efficiency and compliance, properties that are crucial for the control of modern anthropomorphic manipulators. We then propose a new framework of OFC with learned dynamics (OFC-LD) that, unlike classic approaches, does not rely on analytic dynamics functions but rather updates the internal dynamics model continuously from sensorimotor plant feedback. We demonstrate how this approach can compensate for unknown dynamics and for complex dynamic perturbations in an online fashion. A specific advantage of a learned dynamics model is that it contains the stochastic information (i.e., noise) from the plant data, which corresponds to the uncertainty in the system. Consequently one can exploit this information within OFC-LD in order to produce control laws that minimise the uncertainty in the system. In the domain of antagonistically actuated systems this approach leads to improved motor performance, which is achieved by co-contracting antagonistic actuators in order to reduce the negative effects of the noise. Most importantly the shape and source of the noise is unknown a priory and is solely learned from plant data. The model is successfully tested on an antagonistic series elastic actuator (SEA) that we have built for this purpose. The proposed OFC-LD model is not only applicable to robotic systems but also proves to be very useful in the modelling of biological motor control phenomena and we show how our model can be used to predict a wide range of human impedance control patterns during both, stationary and adaptation tasks.

Thesis (Ed. D.)--Montana State University--Bozeman, 2005. / Typescript. Chairperson, Graduate Committee: Richard Howard. Includes bibliographical references (leaves 88-95).

No ambiente universitário, um grande esforço é feito para disponibilizar ferramentas que facilitem aos graduandos, desde o início do curso, a fazer uma correlação entre conteúdos matemáticos abstratos e sua aplicação em uma realidade física, objetivando não apenas a melhor compreensão e fixação de conceitos físicos e matemáticos, mas estimulando o interesse do aluno a estes conceitos. Este trabalho propõe uma ferramenta de simulação de topologias variáveis, que possibilita uma realimentação visual de um sistema descrito através dos dispositivos físicos que o constituem, permitindo ao aluno a interatividade com processo simulado, e uma mais fácil associação com a dinâmica do sistema estudado, pontos-chave na utilização de experimentos para fixação e compreensão de conceitos de controle. Esta ferramenta implementará um algoritmo de simulação em blocos, onde cada bloco é processado individualmente em termos de suas entradas e saídas, proporcionando uma relação direta entre cada aspecto do experimento físico e cada bloco funcional utilizado na simulação. Como os blocos são simulados separadamente, existe também uma relação direta entre as grandezas mensuráveis no experimento físico e as grandezas utilizadas na interligação entre os blocos na simulação. / In the university environment, a great effort is done to make available tools that allow undergraduate students, from the beginning of the course, to correlate abstract mathematical concepts and their application in a physical reality, aiming not only at a better comprehension and fixation of physical and mathematical concepts, but also stimulating the student interest in this concepts. This work proposes a variable topology simulation tool, which allows a visual feedback of a system described using physical devices, enabling the student to interact with the simulated system, key points in the utilization of experiments for the comprehension and fixation of control concepts. This tool will implement an block simulation algorithm , where each block is processed individually, in terms of its inputs and outputs, providing a direct relantionship between between each aspets of the physical experiment and each functional block used in the simulation. Since the blocks are simulated separately, there is also a direct relantionship between the measurable values on the physical experiment and the values used on the interconnection between blocks on the simulation.

No ambiente universitário, um grande esforço é feito para disponibilizar ferramentas que facilitem aos graduandos, desde o início do curso, a fazer uma correlação entre conteúdos matemáticos abstratos e sua aplicação em uma realidade física, objetivando não apenas a melhor compreensão e fixação de conceitos físicos e matemáticos, mas estimulando o interesse do aluno a estes conceitos. Este trabalho propõe uma ferramenta de simulação de topologias variáveis, que possibilita uma realimentação visual de um sistema descrito através dos dispositivos físicos que o constituem, permitindo ao aluno a interatividade com processo simulado, e uma mais fácil associação com a dinâmica do sistema estudado, pontos-chave na utilização de experimentos para fixação e compreensão de conceitos de controle. Esta ferramenta implementará um algoritmo de simulação em blocos, onde cada bloco é processado individualmente em termos de suas entradas e saídas, proporcionando uma relação direta entre cada aspecto do experimento físico e cada bloco funcional utilizado na simulação. Como os blocos são simulados separadamente, existe também uma relação direta entre as grandezas mensuráveis no experimento físico e as grandezas utilizadas na interligação entre os blocos na simulação. / In the university environment, a great effort is done to make available tools that allow undergraduate students, from the beginning of the course, to correlate abstract mathematical concepts and their application in a physical reality, aiming not only at a better comprehension and fixation of physical and mathematical concepts, but also stimulating the student interest in this concepts. This work proposes a variable topology simulation tool, which allows a visual feedback of a system described using physical devices, enabling the student to interact with the simulated system, key points in the utilization of experiments for the comprehension and fixation of control concepts. This tool will implement an block simulation algorithm , where each block is processed individually, in terms of its inputs and outputs, providing a direct relantionship between between each aspets of the physical experiment and each functional block used in the simulation. Since the blocks are simulated separately, there is also a direct relantionship between the measurable values on the physical experiment and the values used on the interconnection between blocks on the simulation.

No ambiente universitário, um grande esforço é feito para disponibilizar ferramentas que facilitem aos graduandos, desde o início do curso, a fazer uma correlação entre conteúdos matemáticos abstratos e sua aplicação em uma realidade física, objetivando não apenas a melhor compreensão e fixação de conceitos físicos e matemáticos, mas estimulando o interesse do aluno a estes conceitos. Este trabalho propõe uma ferramenta de simulação de topologias variáveis, que possibilita uma realimentação visual de um sistema descrito através dos dispositivos físicos que o constituem, permitindo ao aluno a interatividade com processo simulado, e uma mais fácil associação com a dinâmica do sistema estudado, pontos-chave na utilização de experimentos para fixação e compreensão de conceitos de controle. Esta ferramenta implementará um algoritmo de simulação em blocos, onde cada bloco é processado individualmente em termos de suas entradas e saídas, proporcionando uma relação direta entre cada aspecto do experimento físico e cada bloco funcional utilizado na simulação. Como os blocos são simulados separadamente, existe também uma relação direta entre as grandezas mensuráveis no experimento físico e as grandezas utilizadas na interligação entre os blocos na simulação. / In the university environment, a great effort is done to make available tools that allow undergraduate students, from the beginning of the course, to correlate abstract mathematical concepts and their application in a physical reality, aiming not only at a better comprehension and fixation of physical and mathematical concepts, but also stimulating the student interest in this concepts. This work proposes a variable topology simulation tool, which allows a visual feedback of a system described using physical devices, enabling the student to interact with the simulated system, key points in the utilization of experiments for the comprehension and fixation of control concepts. This tool will implement an block simulation algorithm , where each block is processed individually, in terms of its inputs and outputs, providing a direct relantionship between between each aspets of the physical experiment and each functional block used in the simulation. Since the blocks are simulated separately, there is also a direct relantionship between the measurable values on the physical experiment and the values used on the interconnection between blocks on the simulation.

Identifying the causes and consequences of intra-specific variation in cognitive abilities is fundamental to our understanding of the evolution of cognition. The social environment and cognitive abilities appear inextricably linked, yet evidence for how the social environment affects cognitive performances and further, how cognitive performances influence the social environment, has seldom been explored. Using the pheasant, Phasianus colchicus, I explore the relationships between individual variation in cognitive performances in relation to broad and fine-scale structure of the social environment and endeavour to separate cause and consequence. I demonstrate a positive causal effect of the broad-scale social environment on cognitive performances by observing increases in the accuracy of spatial discrimination performances when individuals are in larger groups (Chapter Two and Chapter Four). I show that the positive effects of larger group size occur over a relatively short period (less than one week), suggesting that cognitive performances are flexible in response to the social environment and I suggest four potential mechanisms. I show that while males are part of a social hierarchy, spatial discrimination performances are related to this fine-scale social structure and higher-ranking males outperform lower ranking males (Chapter Three). When attempting to determine cause and consequence, I found that spatial learning performances early in life did not predict adult cognitive performances on the same task or predict their adult social rank (Chapter Four). Hence, my results do not support that social rank is a consequence of spatial learning abilities in male pheasants. The relationship between spatial learning performances and social rank was found in adult males that had their social rank artificially elevated, suggesting that cognitive performances were not simply the result of the current social environment but remain closely related to past agonistic relationships. I did not find a relationship between early life aggression with performances on either a spatial or a non-spatial task in females or males (Chapter Five). This highlights the importance of investigating early life relationships and suggests that the relationship between spatial learning and aggression in adult males may become associated over time as a consequence of further spatial learning experiences, and, or, aggressive interactions. I then demonstrate a consequence of individual variation in cognitive abilities and show that adult foraging associations in the wild disassort by early life cognitive performances (Chapter Six). Individuals with good inhibitory control performance and poor visual discrimination performances were more central in social networks. I propose that differences in cognitive abilities manifest in foraging strategy and influence the resulting social structure. The implications of this predictable social structure remain to be explored. Finally, I discuss these results and how they contribute to our understanding of how the social environment causes individual differences in cognitive performances, as well as how variation in cognitive performances may shape the social environment. I suggest the potential implications of these findings and ideas for future work.

This study examined how student characteristics could be used to predict whether or not a community college student would persist in an online course. The research question guiding the study was, “Which student characteristics can be used to best predict the persistence of community college students in online courses?” The student characteristics examined were learning style, locus of control, computer experience and access, previous online experience and demographics. A survey instrument consisting of two previously developed instruments and a Computer Experience scale that was created by the researcher specifically for this study, was administered to online students at one Florida community college for the pilot study and five additional Florida community colleges for the full study. Confirmatory and exploratory factor analysis were conducted on the computer experience scale to determine if there was an underlying hidden structure. Stepwise logistic regression was completed to determine the student characteristics that were significant predictors of online persistence, as well as an equation that could be used to predict whether or not a community college student would persist in an online course. Confirmatory and exploratory factor analysis revealed that the Computer Experience scale consisted of three underlying subscales. The researcher named the three subscales based on the similarities of the variables that were associated with each factor: Factor one (basic computer skills); Factor two (Internet/email skills); Factor three (interactive computing skills). Three of the initial 25 predictor variables were found to be significant predictors of community college online persistence: GPA, auditory learning style, basic computer skills. An increase in both auditory learning style and basic computer skills was associated with a decrease in the odds of course persistence. On the other hand, an increase in GPA was associated with an increase in the odds of course persistence. Additionally, an equation to predict whether or not an online community college student would persist in an online course was developed. Implications for community college administrators as well as recommendations for future studies are also provided in the study. / Dissertation / PhD

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