Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Derivation of Gain in a Hierarchical Multiple-Goal Pursuit Model(2017) Samuelson, Hannah; Grand, James A; Psychology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The motivational sciences in organizational psychology have recently focused on goal pursuit as a dynamic process, using computational modeling as a methodological tool. This has resulted in a detailed specification of certain components of the goal-pursuit process, leaving others vague. The current research sheds light on one of these underspecified pieces, gain, through the development of the hierarchical multiple-goal pursuit model (HMGPM). The HMGPM proposes that gain, or a goal’s subjectively evaluated importance, is a function of the importance of higher-order goals to which it is connected in an individual’s goal network, and the strength of those connections. Through computational modeling and simulation, the HMGPM is shown to produce theoretically-plausible patterns of goal choice, replicate previous empirical findings, and advance new topics of future research. The usefulness of the HMGPM as a theory-building tool that integrates organizational and social psychological perspectives of motivation is discussed.Item Necessary Bias in Natural Language Learning(2007-05-08) Pearl, Lisa Sue; Weinberg, Amy; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)This dissertation investigates the mechanism of language acquisition given the boundary conditions provided by linguistic representation and the time course of acquisition. Exploration of the mechanism is vital once we consider the complexity of the system to be learned and the non-transparent relationship between the observable data and the underlying system. It is not enough to restrict the potential systems the learner could acquire, which can be done by defining a finite set of parameters the learner must set. Even supposing that the system is defined by n binary parameters, we must still explain how the learner converges on the correct system(s) out of the possible 2^n systems, using data that is often highly ambiguous and exception-filled. The main discovery from the case studies presented here is that learners can in fact succeed provided they are biased to only use a subset of the available input that is perceived as a cleaner representation of the underlying system. The case studies are embedded in a framework that conceptualizes language learning as three separable components, assuming that learning is the process of selecting the best-fit option given the available data. These components are (1) a defined hypothesis space, (2) a definition of the data used for learning (data intake), and (3) an algorithm that updates the learner's belief in the available hypotheses, based on data intake. One benefit of this framework is that components can be investigated individually. Moreover, defining the learning components in this somewhat abstract manner allows us to apply the framework to a range of language learning problems and linguistics domains. In addition, we can combine discrete linguistic representations with probabilistic methods and so account for the gradualness and variation in learning that human children display. The tool of exploration for these case studies is computational modeling, which proves itself very useful in addressing the feasibility, sufficiency, and necessity of data intake filtering since these questions would be very difficult to address with traditional experimental techniques. In addition, the results of computational modeling can generate predictions that can then be tested experimentally.Item Distinguishing Modes of Eukaryotic Gradient Sensing(2005-08-25) Skupsky, Ron; Losert, Wolfgang; Nossal, Ralph J; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The behaviors of biological systems depend on complex networks of interactions between large numbers of components. The network of interactions that allows biological cells to detect and respond to external gradients of small molecules with directed movement is an example where many of the relevant components have been identified. This behavior, called chemotaxis, is essential for biological functions ranging from immune response in higher animals to the food gathering and social behavior of ameboid cells. Gradient sensing is the component of this behavior whereby cells transduce the spatio-temporal information in the external stimulus into an internal distribution of molecules that mediate the mechanical and morphological changes necessary for movement. Signaling by membrane lipids, in particular 3' phosphoinositides (3'PIs), is thought to play an important role in this transduction. Key features of the network of interactions that regulates the dynamics of these lipids are coupled positive feedbacks that might lead to response bifurcations and the involvement of molecules that translocate from the cytosol to the membrane, coupling responses at distant point on the cell surface. Both are likely to play important roles in amplifying cellular responses and shaping their qualitative features. To better understand the network of interactions that regulates 3'PI dynamics in gradient sensing, we develop a computational model at an intermediate level of detail. To investigate how the qualitative features of cellular response depend on the structure of this network, we define four variants of our model by adjusting the effectiveness of the included feedback loops and the importance of translocating molecules in response amplification. Simulations of characteristic responses suggest that differences between our model variants are most evident at transitions between efficient gradient detection and failure. Based on these results, we propose criteria to distinguish between possible modes of gradient sensing in real cells, where many biochemical parameters may be unknown. We also identify constraints on parameters required for efficient gradient detection. Finally, our analysis suggests how a cell might transition between responsiveness and non-responsiveness, and between different modes of gradient sensing, by adjusting its biochemical parameters.