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Dissertations / Theses on the topic 'Computational making'
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1
Sanders, Tom. "Sensory computation and decision making in C. elegans : a computational approach." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/15442/.
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In Caenorhabditis elegans (C. elegans) and in neuroscience generally, a hierarchical view of nervous systems prevails. Roughly speaking, sensory neurons encode the external environment, interneurons encode internal state and decisions, and motor neurons encode muscle activation. Here, using an integrated approach to model sensory computation and decision making in C. elegans, I show a striking phenomenon. Via the simplest modulation possible, sensitization and desensitization, sensory neurons in C. elegans can also encode the animal’s internal state. In this thesis, I present a modeling framework, and use it to implement two detailed models of sensory adaptation and decision making. In the first model I consider a decision making task, in which worms need to cross a lethal barrier in order to reach an attractant on the other side. My model captures the experimental results, and predicts a minimal set of requirements. This model‘s mechanism is reminiscent of similar top-down attention modulation motifs in mammalian cortex. In the second model, I consider a form of plasticity in which animals alternate their perception of a signal from attractive to repulsive. I show how the model encodes high and low-level behavioral states, balancing attraction and aversion, exploration and exploitation, pushing the ‘decision making’ into the sensory layer. Furthermore, this model predicts that specific sensory neurons may have the capacity to selectively control distinct motor programs. To accomplish these results, the modeling framework was designed to simulate a full sensory motor pathway and an in silico simulation arena, allowing it to reproduce experimental findings from multiple assays. Hopefully, this allows the model to be used by the C. elegans community and to be extended, bringing us closer to the larger aim of understanding distributed computation and the integrated neural control of behavior in a whole animal.
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Heller, Collin M. "A computational model of engineering decision making." Thesis, Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50272.
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The research objective of this thesis is to formulate and demonstrate a computational framework for modeling the design decisions of engineers. This framework is intended to be descriptive in nature as opposed to prescriptive or normative; the output of the model represents a plausible result of a designer's decision making process. The framework decomposes the decision into three elements: the problem statement, the designer's beliefs about the alternatives, and the designer's preferences. Multi-attribute utility theory is used to capture designer preferences for multiple objectives under uncertainty. Machine-learning techniques are used to store the designer's knowledge and to make Bayesian inferences regarding the attributes of alternatives. These models are integrated into the framework of a Markov decision process to simulate multiple sequential decisions. The overall framework enables the designer's decision problem to be transformed into an optimization problem statement; the simulated designer selects the alternative with the maximum expected utility. Although utility theory is typically viewed as a normative decision framework, the perspective in this research is that the approach can be used in a descriptive context for modeling rational and non-time critical decisions by engineering designers. This approach is intended to enable the formalisms of utility theory to be used to design human subjects experiments involving engineers in design organizations based on pairwise lotteries and other methods for preference elicitation. The results of these experiments would substantiate the selection of parameters in the model to enable it to be used to diagnose potential problems in engineering design projects.
The purpose of the decision-making framework is to enable the development of a design process simulation of an organization involved in the development of a large-scale complex engineered system such as an aircraft or spacecraft. The decision model will allow researchers to determine the broader effects of individual engineering decisions on the aggregate dynamics of the design process and the resulting performance of the designed artifact itself. To illustrate the model's applicability in this context, the framework is demonstrated on three example problems: a one-dimensional decision problem, a multidimensional turbojet design problem, and a variable fidelity analysis problem. Individual utility functions are developed for designers in a requirements-driven design problem and then combined into a multi-attribute utility function. Gaussian process models are used to represent the designer's beliefs about the alternatives, and a custom covariance function is formulated to more accurately represent a designer's uncertainty in beliefs about the design attributes.
3
Findling, Charles. "Computational learning noise in human decision-making." Thesis, Sorbonne université, 2018. http://www.theses.fr/2018SORUS490.
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Dans des environnements incertains et changeants, prendre des décisions nécessite l’analyse et la pondération des informations passées et présentes. Pour modéliser le comportement humain dans de tels environnements, des approches computationnelles de l’apprentissage ont été développées basées sur l’apprentissage par renforcement ou de l’inférence bayésienne. Afin de mieux prendre en compte la variabilité comportementale, ces approches supposent un bruit dans la sélection de l’action. Dans la première partie de mon travail, j’argumente que le bruit dans la sélection de l’action est insuffisant pour expliquer la variabilité comportementale et je montre la présence de bruit d'apprentissage reflétant des imprécisions de calcul. À cette fin, j’introduis du bruit dans l'algorithme d'apprentissage en donnant la possibilité d'écarts aléatoires de la règle de mise à jour sans bruit. L'ajout de ce bruit permet de mieux expliquer les performances comportementales humaines (Findling C., Skvortsova V., et al., 2018a, en préparation). Dans la deuxième partie de mon travail, je montre que ce bruit possède des propriétés adaptatives vertueuses dans les processus d’apprentissage suscités dans des environnements changeants (volatiles). En utilisant le cadre de modélisation Bayésien, je démontre qu’un modèle d’apprentissage simple, faisant l’hypothèse de contingences externes stables, mais avec du bruit dans l’apprentissage, est aussi performant que le modèle Bayésien optimal qui infèrent la volatilité de l’environnement. En outre, j’établis que ce modèle de bruit explique mieux le comportement humain dans des environnements changeants (Findling C. et al., 2018b, en préparation)In uncertain and changing environments, making sequential decisions requires analyzing and weighting the past and present information. To model human behavior in such environments, computational approaches to learning have been developed based on reinforcement learning or Bayesian inference. To further account for behavioral variability, these computational approaches assume action selection noise, usually modeled with a softmax function. In the first part of my work, I argue that action selection noise is insufficient to explain behavioral variability and show the presence of learning noise reflecting computational imprecisions. To this end, I introduced computational noise in the standard reinforcement learning algorithm through random deviations in the noise-free update rule. Adding this noise led to a better account of human behavioral performances in reward-guided tasks (Findling C., Skvortsova V., et al., 2018a, in prep). The presence of learning noise led me to investigate whether this noise could have a functional role. In the second part of my work, I argue that this learning noise actually has virtuous adaptive properties in learning processes elicited in changing (volatile) environments. Using the Bayesian modeling framework, I demonstrate that a simple learning model assuming stable external contingencies with learning noise performs virtually as well as the optimal Bayesian adaptive process based on inferring the volatility of the environment. Furthermore, I establish that this learning noise model better explains human behavioral performances in changing environments (Findling C. at al., 2018b, in prep)
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D'Ambrosio, Catherine P. "Computational representation of bedside nursing decision-making processes /." Thesis, Connect to this title online; UW restricted, 2003. http://hdl.handle.net/1773/7266.
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Yuan, Fan. "Modeling and computational strategies for medical decision making." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54857.
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In this dissertation, we investigate three topics: predictive models for disease diagnosis and patient behavior, optimization for cancer treatment planning, and public health decision making for infectious disease prevention. In the first topic, we propose a multi-stage classification framework that incorporates Particle Swarm Optimization (PSO) for feature selection and discriminant analysis via mixed integer programming (DAMIP) for classification. By utilizing the reserved judgment region, it allows the classifier to delay making decisions on ‘difficult-to-classify’ observations and develop new classification rules in later stage. We apply the framework to four real-life medical problems: 1) Patient readmissions: identifies the patients in emergency department who return within 72 hours using patient’s demographic information, complaints, diagnosis, tests, and hospital real-time utility. 2) Flu vaccine responder: predicts high/low responders of flu vaccine on subjects in 5 years using gene signatures. 3) Knee reinjection: predicts whether a patient needs to take a second surgery within 3 years of his/her first knee injection and tackles with missing data. 4) Alzheimer’s disease: distinguishes subjects in normal, mild cognitive impairment (MCI), and Alzheimer’s disease (AD) groups using neuropsychological tests. In the second topic, we first investigate multi-objective optimization approaches to determine the optimal dose configuration and radiation seed locations in brachytherapy treatment planning. Tumor dose escalation and dose-volume constraints on critical organs are incorporated to kill the tumor while preserving the functionality of organs. Based on the optimization framework, we propose a non-linear optimization model that optimizes the tumor control probability (TCP). The model is solved by a solution strategy that incorporates piecewise linear approximation and local search.
In the third topic, we study optimal strategies for public health emergencies under limited resources. First we investigate the vaccination strategies against a pandemic flu to find the optimal strategy when limited vaccines are available by constructing a mathematical model for the course of the 2009 H1N1 pandemic flu and the process of the vaccination. Second, we analyze the cost-effectiveness of emergency response strategies again a large-scale anthrax attack to protect the entire regional population.
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Bacharidou, Maroula. "Active prototyping : a computational framework for designing while making." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118501.
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Thesis: S.M. in Architecture Studies, Massachusetts Institute of Technology, Department of Architecture, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 89-92).
In the wake of an increased accessibility of rapid prototyping tools in design education and practice, designers still face a series of challenges related to their use, one of them being the way in which they use these machines to actively explore and enhance their ideas. At the same time, the concepts of continuous interaction with computational fabrication tools and design exploration through physical prototyping are gaining impetus in computational design research and human-computer interaction. Stimulated by these inquiries, the hypothesis of this thesis is that physical prototyping tools can be used as tools for active design exploration and evaluation. Towards this goal, I introduce Active Prototyping, a framework for enhancing physical engagement with design objects by aiding the designer to project the impact of tools on design outcomes and explore a range of possible design solutions while making. Active Prototyping integrates the following operations: (a) physical control of a fabrication device, (b) recording of designer actions while using the device (c) visual exploration of possible design solutions while developing a physical prototype and (d) machine feedback on the prototyping of selected design solutions. To demonstrate the Active Prototyping framework, I develop Fabcorder, a technical apparatus that implements a number of the above operations. Through application examples, I demonstrate how Active Prototyping can render physical prototyping processes more exploratory and digital fabrication processes more intuitive. I conclude by proposing action recording and generative methods as two novel additions to existing frameworks for computational design and fabrication that can bring future tool-making strategies into a more creative context.
by Maroula Bacharidou.
S.M. in Architecture Studies
7
Mancinelli, Federico. "Models of decision making and behavioural control in computational psychiatry." Thesis, University College London (University of London), 2018. http://discovery.ucl.ac.uk/10046131/.
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Computational psychiatry, which is a recent area of research, involves the use of statistical and computational methods to investigate human psychopathology and the brain. Here, I present models of human behaviour in tasks where subjects expressed preferences between options characterised by orthogonal amounts and odds of monetary rewards and different degrees and demands on behavioural control. We first model an experiment which examined subjects’ propensity to gamble for different odds and amounts of reward in the face of Acute Tryptophan Depletion (ATD). Our computational approach supported existing statistical evidence that specific serotonin receptor types might mediate the effects of ATD on the sensitivity of subjects to rewards. We also showed that subjects’ choices were influenced by the weighted sum of the probability and the amount, rather than by their interaction, as required by conventional prospect theory. In the remainder of the thesis, we present the design, realisation, and analysis of a novel task for humans in which choice was tied to perceived behavioural control (as measured by Hannah Levenson’s multi-dimensional Locus of Control scales). The genesis of this task was an attempt to capture aspects of controllability relevant to psychopathology in depression. In the task, the winning amounts were made explicit to subjects, but the odds, and the extent to which these depended on their decisions and efforts, were learned. We used computational modelling to interpret various measures of choice, choice evolution, and indecision in the task.
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Cartwright, Daniel R. "Digital decision-making : using computational argumentation to support democratic processes." Thesis, University of Liverpool, 2011. http://livrepository.liverpool.ac.uk/2993/.
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One of the key questions facing governments around the world is that of how to increase and maintain the engagement of citizens in democratic processes. Recent thought, both within academia and government itself, has turned to the use of modern computational technology to provide citizens with access to democratic processes. Access to computer and Internet technology by the general public has vastly increased over the past decade, and this wide access is one of a number of motivations behind research into the provision of democratic tasks and processes online. The particular democratic process that forms the focus of this thesis is that of online opinion gathering in order to aid government decision making. The provision of mechanisms to gather and analyse public opinion is important to any government which claims to promote a fair and equal democracy, as decisions should be made in consideration of the views and opinions of the citizens of such a democracy. The work that comprises this thesis is motivated by existing research into harvesting opinion through a variety of online methods. The software tools available largely fall into one of two categories: Those which are not based on formal structure, and those which are based on an underlying formal model of argument. The work presented in this thesis aims to overcome the shortfalls inherent to both of these categories of tool in order to realise a software suite to support both the process of opinion gathering, and analysis of the resulting data. This is achieved through the implementation of computational models of argument from the research area of argumentation, with special consideration as to how these models can be used in implemented systems in a manner that allows laypersons to interact with them effectively. A particular model of argument which supports the process of practical reasoning is implemented in a web-based computer system, thus allowing for the collection of structured arguments which are later analysed according to formal models of argument visualisation and evaluation. The theories underlying the system are extended in order to allow for added expressivity, thus providing a mechanism for more life-like argument within a system which supports comprehensive computational analysis. Ultimately, the contributions of this thesis are a functional system to support an important part of the democratic process, and an investigation into how the underlying theories can be built upon and extended in order to promote expressive argumentation.
9
Huang, He. "Decision-making and motor control| computational models of human sensorimotor processing." Thesis, University of California, San Diego, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3673994.
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To survive and effectively interact with the environment, human sensorimotor control system collects sensory information and acts based on the state of the world. Human behavior can be considered and studied at discrete time or continuous time. For the former, human makes discrete categorical decisions when presented with different alternative choices (e.g. choose Left or Right at an intersection). For the later, humans plan and execute continuous movements when instructed to perform a motor task (e.g. drive to a destination). In this dissertation we examine human behavior at both levels. Part I focuses on understanding decision-making at discrete time using Bayesian Models. We start by investigating the influence of environmental statistics in a saccadic visual search ask, in which we use a dynamic belief model to describe subjects' learning process of the environment statistics cross-trials. Then we look at a special effect of decision- making, the sequential effect, and apply the dynamic belief model to explain subjects' cross-trial learning and a drift diffusion model to explain their within-trial decision- making process. Part II focuses on examining motor control at continuous time using Optimal Control Theory. We start by investigating the objective functions in oculomotor control (saccadic eye movement, smooth pursuit, and applications in eye-hand coordination) with an infomax model. Then we apply inverse optimal control model to study impaired motor behavior in depressed individuals. In particular, we present a framework based on optimal control theory, which can distinguish the effects of sensorimotor speed, goal setting and motivational factors in goal-directed motor tasks. Finally, we propose to use facial expression as another measure of the emotional state in depressed individuals, which can be used to provide further understanding of the behavior and model parameters estimated from the proposed inverse framework.
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Keel, Paul E. (Paul Erich). "Knowledge trading : computational support for individual and collaborative sense-making activities." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/28807.
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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Architecture, 2004.Includes bibliographical references (leaves 127-132).
(cont.) outlined. 2. Demonstration that computer systems can use the discovered relations among data items to help users search for relevant information, prioritize the data exchange between collaborating users, and visualize data in various ways. This investigation looks at how a human's increasing knowledge about a problem space is influential in the subsequent accumulation of new data. The findings are converted into computational equivalents that can support individual and collaborative sense-making processes.
This dissertation explores the potential for computational systems to analyze and support individual and collaborative human sense-making activities. In this context human sense-making refers to the act of mentally and physically relating pieces of information so as to develop an understanding of a particular situation. Human sense-making activities such as brainstorming, decision-making, and problem solving sessions often produce a lot of data such as notes, sketches, and documents. The participants of sense-making activities usually develop a good understanding of the relations among these individual data items. These relations define the context. Because the relations remain within the minds of the participants they are neither accessible to outsiders and computational systems nor can they be recorded or backed up. This dissertation outlines a first set of computational mechanisms that construct relations from the spatial arrangement, use, and storage of data items. A second set of computational mechanisms takes advantage of these relations by helping users to keep track of, search for, exchange, arrange, and visualize data items. The computational mechanisms are both adaptive and evocative, meaning that the computational mechanisms dynamically adapt to users and changing circumstances while also trying to influence the human sense-making process. Contributions: 1. Demonstration that computer systems can discover probable relations among data items from their spatial arrangement and use by users. This work identifies and analyzes various human mental processes involved in the determination of possible relations among data items such as documents on a work desk or files in a computer system. A computational equivalent is proposed for every mental process
by Paul Erich Keel.
Ph.D.
11
Lewis, Whitney E. "Design Scaffolding for Computational Making in the Visual Programming Tool ARIS." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7235.
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In this thesis, I explore how design scaffolds, or (i.e., intellectual supports) can assist learners engaging with computational making processes. Computational making combines programming with artifact production. Due to the complexity of tasks involved in computational making, there is an increasing need to explore and develop support systems for learners engaging with computational making.
With $3,000 funding from Utah State University’s College of Education and Human Services, an undergraduate researcher and I, who both have experience with youth and computational making research, explored how design scaffolds impact youth engaging with computational making processes. To do so, we held a workshop where 11 learners (11 female, ages 11-16) used ARIS, a platform designed for non-programmers to create mobile games. In addition, we interviewed five ARIS designers who were able to evaluate our design scaffolds.
We provide insights for improving the use of design scaffolds in computational making with ARIS specifically that also apply broadly to computational making processes. Moreover, we developed an ARIS course that teaches educators to use a design scaffold tool for ARIS. This research provides immediate benefits for educators who access the ARIS course and researchers seeking to improve upon design scaffold research for computational making processes.
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Keel, Paul Erich. "Knowledge trading : computational support for individual and collaborative sense-making activities." Thesis, Monterey, California. Naval Postgraduate School, 2004. http://hdl.handle.net/10945/1201.
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This dissertation explores the potential for computational systems to analyze and support individual and collaborative human sense-making activities. In this context human sense-making refers to the act of mentally and physically relating pieces of information so as to develop an understanding of a particular situation. Human sense-making activities such as brainstorming, decision-making, and problem solving sessions often produce a lot of data such as notes, sketches, and documents. The participants of sense-making activities usually develop a good understanding of the relations among these individual data items. These relations define the context. Because the relations remain within the minds of the participants they are neither accessible to outsiders and computational systems nor can they be recorded or backed up. This dissertation outlines a first set of computational mechanisms that construct relations from the spatial arrangement, use, and storage of data items. A second set of computational mechanisms takes advantage of these relations by helping users to keep track of, search for, exchange, arrange, and visualize data items. The computational mechanisms are both adaptive and evocative, meaning that the computational mechanisms dynamically adapt to users and changing circumstances while also trying to influence the human sense-making process.
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Xia, Shang. "A computational study on vaccination decision making for infectious disease control." HKBU Institutional Repository, 2013. http://repository.hkbu.edu.hk/etd_ra/1527.
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Lieuw, Iris. "Time Frequency Analysis of Neural Oscillations in Multi-Attribute Decision-Making." Scholarship @ Claremont, 2015. http://scholarship.claremont.edu/scripps_theses/556.
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In our daily lives, we often make decisions that require the use of self-control, weighing trade-offs between various attributes: for example, selecting a food based on its health rather than its taste. Previous research suggests that re-weighting attributes may rely on selective attention, associated with decreased neural oscillations over posterior brain regions in the alpha (8-12 Hz) frequency range. Here, we utilized the high temporal resolution and whole-brain coverage of electroencephalography (EEG) to test this hypothesis in data collected from hungry human subjects exercising dietary self-control. Prior analysis of this data has found time-locked neural activity associated with each food’s perceived taste and health properties from approximately 400 to 650 ms after stimulus onset (Harris et al., 2013). We conducted time-frequency analyses to examine the role of alpha-band oscillations in this attribute weighting. Specifically, we predicted that there would be decreased alpha power in posterior electrodes beginning approximately 400 ms after stimulus onset for the presentation of healthy food relative to unhealthy food, reflecting shifts in selective attention. Consistent with this hypothesis, we found a significant decrease in alpha power for presentations of healthy relative to unhealthy foods. As predicted, this effect was most pronounced at posterior occipital and parietal electrodes and was significant from approximately 450 to 700 ms post-stimulus onset. Additionally, we found significant alpha-band decreases in right temporal electrodes during these times. These results extend previous attention research to multi-attribute choice, suggesting that the re-weighting of attributes can be measured neuro-computationally.
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Smith, Stephanie Marie. "Understanding decision making with process-tracing methods." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1562877539274665.
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Hobbs, Reginald L. (Reginald Lionel). "A Scenario-directed Computational Framework To Aid Decision-making And Systems Development." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7251.
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Scenarios are narratives that illustrate future possibilities or existing systems, and
help policy makers and system designers choose among alternative courses of action. Scenario-based decision-making crosses many domains and multiple perspectives. Domain-specic
techniques for encoding, simulating, and manipulating scenarios exist, however there is no
general-purpose scenario representation capable of supporting the wide spectrum of formality
from executable simulation programs to
free-form text to streaming media descriptions.
The claim of this research is that there is a computer readable scenario framework that can
capture the semantics of a problem domain and
make scenarios an active part of decision
making. The challenge is to define a representation for scenarios that supports a
wide range of discussion and comprehension activities while remaining independent of content and access mechanisms. This dissertation describes a scenario ontology derived by examining alternate
forms of narrative: thought experiments, mental models, case-based reasoning, use cases,
design patterns, screenwriting, film-editing, intelligent agents, and other narrative domains.
The scenario conceptual model was based on an analysis of forms of narrative and the activities of storytelling. This method separates what a narrative is from how it is used. The research contribution is the development of the hyperscenario framework. A hyperscenario
is a scenario representation containing link structures for navigation between scenario elements. The hyperscenario framework consists of the scenario ontology, scenario grammar,
and a scenario specification called Scenario Markup Language (SCML). The results of the
web-enabled simulation experiment validate the improvement on decision-making due to the
hyperscenario framework.
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Harrison, Michael J. "The enhancement of intra-operative diagnostics and decision-making using computational methods." Thesis, University of Auckland, 2005. http://hdl.handle.net/2292/74.
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The data presented and views expressed in this document are the result of multiple published and unpublished studies over the last 25 years. My over-arching goal in this research was to use modern computing power to create functionally useful diagnoses, in real time, from the monitoring systems used during routine anaesthesia and to present these diagnoses in an ergonomic manner. In addition it was intended to incorporate into the anaesthetic monitor, expert systems that help with the management of uncommon situations. The Australian and New Zealand College guidelines on monitoring during anaesthesia dictate those measurements that should be made during every anaesthetic; from these data evidence can be gathered, integrated, and presented to the clinician. Constraints in this field of research include the inability of the monitors to see, hear or understand the context of operating theatre activities, and computer processing time. Because many studies are involved the methods are detailed in the main text, and are not summarized here. Physiological 'envelopes' have been developed, in which the 'normal' variation in physiological variables, during anaesthesia, are enclosed. They have enabled the creation of intelligent alarm systems that can suggest diagnoses. A retrospective off-line study showed that it was possible to diagnose the onset of malignant hyperpyrexia, using fuzzy logic templates, about 10minutes earlier than the clinician. Some variables may be more important than others in making a diagnosis, and the strength of a diagnosis depends on the amount of supporting evidence, the amount of evidence not against the diagnosis and the amount of missing data. Decision-making (for example to transfuse or not transfuse blood) can also be mathematically modelled so that decision making is more consistent. Finally, investigation of the ways of displaying data indicates that the output can be very explicit. My overall conclusion is that real time decision support systems for the management of clinical dilemmas are possible. They can be instantly and easily accessible and can sit discretely in the background of anaesthetic monitors to be activated at will by the anaesthetist.
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El-Zanfaly, Dina Ezz ElDin. "[I³] imitation, iteration and improvisation : embodied interaction in computational making and learning." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/118695.
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Thesis: Ph. D. in Architecture: Design and Computation, Massachusetts Institute of Technology, Department of Architecture, 2018.Cataloged from PDF version of thesis.
Includes bibliographical references (pages 122-127).
Despite advances in digital design and fabrication technologies, creative design practices still follow Alberti's separation of the design phase from the construction phase. This separation causes a reliance on digital fabrication machines that pushes human agency to the periphery of the making process. The interfaces of these technologies and their linear process of production create cognitive and perceptual obstacles, making it difficult for non-experts to create and improvise independently. Design and the ability to make are often thought to be intuitive, yet significant research has suggested that intuition is developed through skilled practice, interaction with materials, tools, and machines. Existing pedagogical approaches to design focus on outcomes and instructors' feedback to the students, neglecting the importance of the tools and the process itself. How, then, do we learn to make something? What are the potential roles of computational tools, theories, and practices in understanding, describing, and enriching the making and learning process? What can we learn from machines, and what can machines learn from us? Finally, what do we learn from making? Here, I introduce l³, a computational making methodology that enables emerging designers and makers to improvise and create on their own. I call this method F for its three-layer operation of Imitation, Iteration and Improvisation. Drawing upon research from other fields, this methodology for human-machine making and learning is based on a recursive process of embodied, situated interaction between learners, machines, materials, and the things-in-the-making. I describe the continuous process of developing and testing 1³ through experiments I conducted during the teaching of three courses for graduate and undergraduate students. The qualitative research I conducted shows that through using the 1³ methodology, students develop their spatial reasoning and decision-making skills while at the same time learning to use digital technologies as design companions.
by Dina Ezz ElDin El-Zanfaly.
Ph. D. in Architecture: Design and Computation
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Panjwani, Alisha (Alisha Hasan). "From storytelling to story making : children creating stories with tangible computational media." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/98621.
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Thesis: S.M., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2015.Page 96 blank. Cataloged from PDF version of thesis.
Includes bibliographical references (pages 93-95).
Storytelling and making are two of the oldest forms of self-expression. Through stories we give meaning to our everyday experiences making sense of our world. By making physical objects, we can create tangible representations of our ideas that we can share with others. This thesis investigates how to introduce children and educators to StoryMaking, a process that cultivates creative learning by combining new forms of storytelling and new forms of making with technology. In this research, I highlight the process of StoryMaking, exploring ways for children to make physical representations of their personal stories using Tangible Computational Media (T1CM) - a medium that enables them to design and create physical objects with interactive and dynamic behaviors. Through workshops I observed children's StoryMaking explorations with three forms of TCM: paper electronics, programmable projections, and sewable circuits. Based on my observations and the analysis of the artifacts, I share how children represented their personal experiences artistically, electronically, and computationally. Through case studies, I reflect on my experiences facilitating these StoryMaking workshops. Based on these experiences, I describe ten design practices that can help other educators design and facilitate StoryMaking experiences in their learning settings.
by Alisha Panjwani.
S.M.
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Novaro, Arianna. "Collective decision-making with goals." Thesis, Toulouse 3, 2019. http://www.theses.fr/2019TOU30179.
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Des agents devant prendre une décision collective sont souvent motivés par des buts individuels. Dans ces situations, deux aspects clés doivent être abordés : sélectionner une alternative gagnante à partir des voix des agents et s'assurer que les agents ne manipulent pas le résultat. Cette thèse étudie l'agrégation et la dimension stratégique des décisions collectives lorsque les agents utilisent un langage représenté de manière compacte. Nous étudions des langages de type logique : de la logique propositionnelle aux CP-nets généralisés, en passant par la logique temporelle linéaire (LTL). Notre principale contribution est l'introduction d'un cadre de vote sur les buts, dans lequel les agents soumettent des buts individuels exprimés comme des formules de la logique propositionnelle. Les fonctions d'agrégation classiques issues du vote, de l'agrégation de jugements et de la fusion de croyances sont adaptées et étudiées de manière axiomatique et computationnelle. Les propriétés axiomatiques connues dans la littérature sur la théorie du choix social sont généralisées à ce nouveau type d'entrée, ainsi que les problèmes de complexité visant à déterminer le résultat du vote. Une autre contribution importante est l'étude de l'agrégation des CP-nets généralisés, c'est-à-dire des CP-nets où la précondition de l'énoncé de préférence est une formule propositionnelle. Nous utilisons différents agrégateurs pour obtenir un classement collectif des résultats possibles. Grâce à cette thèse, deux axes de recherche sont ainsi reliés : l'agrégation des CP-nets classiques et la généralisation des CP-nets à des préconditions incomplètes. Nous contribuons également à l'étude du comportement stratégique dans des contextes de prise de décision collective et de théorie des jeux. Le cadre du vote basé sur les buts est de nouveau étudié sous l'hypothèse que les agents peuvent décider de mentir sur leur but s'ils obtiennent ainsi un meilleur résultat. L'accent est mis sur trois règles de vote majoritaires qui se révèlent manipulables. Par conséquent, nous étudions des restrictions à la fois sur le langage des buts et sur les stratégies des agents en vue d'obtenir des résultats de votes non manipulables. Nous présentons par ailleurs une extension stratégique d'un modèle récent de diffusion d'opinion sur des réseaux d'influence. Dans les jeux d'influence définis ici, les agents ont comme but des formules en LTL et ils peuvent choisir d'utiliser leur pouvoir d'influence pour s'assurer que leur but est atteint. Des solutions classiques telles que la stratégie gagnante sont étudiées pour les jeux d'influence, en relation avec la structure du réseau et les buts des agents. Enfin, nous introduisons une nouvelle classe de concurrent game structures (CGS) dans laquelle les agents peuvent avoir un contrôle partagé sur un ensemble de variables propositionnelles. De telles structures sont utilisées pour interpréter des formules de logique temporelle en temps alternés (ATL), grâce auxquelles on peut exprimer l'existence d'une stratégie gagnante pour un agent dans un jeu itéré (comme les jeux d'influence mentionnés ci-dessus). Le résultat principal montre qu'un CGS avec contrôle partagé peut être représenté comme un CGS avec contrôle exclusif. En conclusion, cette thèse contribue au domaine de la prise de décision collective en introduisant un nouveau cadre de vote basé sur des buts propositionnels. Elle présente une étude de l'agrégation des CP-nets généralisés et une extension d'un cadre de diffusion d'opinion avec des agents rationnels qui utilisent leur pouvoir d'influence. Une réduction du contrôle partagé à un contrôle exclusif dans les CGS pour l'interprétation des logiques du raisonnement stratégique est également proposée. Par le biais de langages logiques divers, les agents peuvent ainsi exprimer buts et préférences sur la décision à prendre, et les propriétés souhaitées pour le processus de décision peuvent en être garantiesAgents having to take a collective decision are often motivated by individual goals. In such scenarios, two key aspects need to be addressed. The first is defining how to select a winning alternative from the expressions of the agents. The second is making sure that agents will not manipulate the outcome. Agents should also be able to state their goals in a way that is expressive, yet not too burdensome. This dissertation studies the aggregation and the strategic component of multi-agent collective decisions where the agents use a compactly represented language. The languages we study are all related to logic: from propositional logic, to generalized CP-nets and linear temporal logic (LTL). Our main contribution is the introduction of the framework of goal-based voting, where agents submit individual goals expressed as formulas of propositional logic. Classical aggregation functions from voting, judgment aggregation, and belief merging are adapted to this setting and studied axiomatically and computationally. Desirable axiomatic properties known in the literature of social choice theory are generalized to this new type of propositional input, as well as the standard complexity problems aimed at determining the result. Another important contribution is the study of the aggregation of generalized CP-nets coming from multiple agents, i.e., CP-nets where the precondition of the preference statement is a propositional formula. We use different aggregators to obtain a collective ordering of the possible outcomes. Thanks to this thesis, two lines of research are thus bridged: the one on the aggregation of complete CP-nets, and the one on the generalization of CP-nets to incomplete preconditions. We also contribute to the study of strategic behavior in both collective decision-making and game-theoretic settings. The framework of goal-based voting is studied again under the assumption that agents can now decide to submit an untruthful goal if by doing so they can get a better outcome. The focus is on three majoritarian voting rules which are found to be manipulable. Therefore, we study restrictions on both the language of the goals and on the strategies allowed to the agents to discover islands of strategy-proofness. We also present a game-theoretic extension of a recent model of opinion diffusion over networks of influence. In the influence games defined here, agents hold goals expressed as formulas of LTL and they can choose whether to use their influence power to make sure that their goal is satisfied. Classical solution concepts such as weak dominance and winning strategy are studied for influence games, in relation to the structure of the network and the goals of the agents. Finally, we introduce a novel class of concurrent game structures (CGS) in which agents can have shared control over a set of propositional variables. Such structures are used for the interpretation of formulas of alternating-time temporal logic, thanks to which we can express the existence of a winning strategy for an agent in a repeated game (as, for instance, the influence games mentioned above). The main result shows by means of a clever construction that a CGS with shared control can be represented as a CGS with exclusive control. In conclusion, this thesis provides a valuable contribution to the field of collective decision-making by introducing a novel framework of voting based on individual propositional goals, it studies for the first time the aggregation of generalized CP-nets, it extends a framework of opinion diffusion by modelling rational agents who use their influence power as they see fit, and it provides a reduction of shared to exclusive control in CGS for the interpretation of logics of strategic reasoning. By using different logical languages, agents can thus express their goals and preferences over the decision to be taken, and desirable properties of the decision process can be ensured
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Pettit, Elizabeth Jean. "Using Process Tracing and Computational Modeling to Investigate Cognition During Risky Decision Making." Miami University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=miami1619098470233094.
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Albantakis, Larissa. "Decision-making beyond “left or right”. A computational study on the neurophysiology behind multiple-choice decision-making and choice reevaluation." Doctoral thesis, Universitat Pompeu Fabra, 2011. http://hdl.handle.net/10803/52754.
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Neurophysiological brain processes during perceptual decision-making have mainly been investigated under the simplified conditions of two-alternative forced-choice (2AFC) tasks. How do established principles of decision-making, obtained from these simple binary tasks, extend to more complex aspects like multiple choice-alternatives and changes of mind? Here, we first address this question theoretically: based on recent experimental findings, we extend a biophysically realistic attractor model of decision-making to account for multiple choice-alternatives and choice reevaluation. Moreover, we complement our computational approach by a psychophysical experiment, exploring how changes of mind depend on the number of choice-alternatives. Our results affirm the general conformance of attractor networks with higher-level neural processes. In particular, we found evidence for the physiological relevance of a so far unregarded bifurcation. Furthermore, our findings suggest an advantage of a pooled multi-neuron representation of choice-alternatives, and a negative correlation between reaction time and changes of mind, possibly regulated by the decision threshold. Finally, we gained testable predictions on neural firing rates during changes of mind and propose future experiments to distinguish nonlinear attractor from linear diffusion models.Los procesos neurofisiológicos que tienen lugar en el cerebro durante la toma de decisiones basadas en fenómenos de percepción han sido investigados, principalmente, en condiciones simplificadas, en particular, de tareas con dos alternativas y elección forzada (2AFC). ¿Cómo podemos extender los principios establecidos sobre la toma de decisiones obtenidas a partir de estas tareas simples y binarias, a aspectos más complejos como decisiones con alternativas múltiples y los cambios de opinión? En esta tesis, en primer lugar, abordamos esta cuestión de manera teórica: a partir de resultados experimentales recientes, extendemos un modelo de toma de decisiones, que es un modelo con atractores realista desde el punto de vista biofísico, con el objetivo de explicar la elección con alternativas múltiples y la reevaluación de la elección. Además, complementamos nuestro enfoque computacional con un experimento psicofísico, explorando cómo los cambios de opinión dependen del número de alternativas. Nuestros resultados refuerzan la tesis de que existe una correspondencia general entre las redes de atractores y los procesos neuronales superiores. En particular, revelan la importancia fisiológica de una bifurcación que hasta ahora ha pasado inadvertida. Además, sugieren la ventaja de representar las alternativas de elección con múltiples neuronas, y la existencia de una correlación negativa entre el tiempo de reacción y los cambios de opinión, posiblemente regulada por el umbral de decisión. Finalmente, proporcionamos predicciones comprobables sobre las tasas de disparo neuronal durante los cambios de la opinión y proponemos experimentos futuros para distinguir los modelos no lineales con atractores de los modelos de difusión lineal.
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Konstantinidis, E. "The role of unconscious influences on decision-making under uncertainty : behavioural and computational approaches." Thesis, University College London (University of London), 2014. http://discovery.ucl.ac.uk/1454099/.
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How do people make decisions in uncertain environments and what types of knowl- edge control their choices? Can our decisions be guided by unconscious influences or intuitive “gut” feelings? According to the Somatic Marker Hypothesis, a popular account of the role of affect in decision-making, emotion-based signals can guide our decisions in uncertain environments outside awareness. However, evidence for this claim can be ques- tioned on the grounds of inadequate and insensitive assessments of conscious knowledge. In this work, variations of a classic experience-based decision-making paradigm, the Iowa Gambling Task (IGT), are employed in combination with subjective measures of awareness in order to investigate the role played by unconscious influences. Specifically, the validity of post-decision wagering as a sensitive and bias-free measure of conscious content is examined and contrasted to confidence ratings and quantitative reports. The results demonstrate the inadequacy of post-decision wagering as a direct measure of conscious knowledge and also question the claim that implicit processes influence decision- making. In order to measure and understand the cognitive and psychological processes un- derlying performance on the IGT, computational modeling analyses are undertaken to provide deeper insights into the dynamics of decision-making. Reinforcement-learning models are evaluated using different model comparison techniques and a computational model of confidence ratings in decision-making under uncertainty is developed.
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Cui, Zhuoya. "Understanding social function in psychiatric illnesses through computational modeling and multiplayer games." Diss., Virginia Tech, 2021. http://hdl.handle.net/10919/103528.
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Impaired social functioning conferred by mental illnesses has been constantly implicated in previous literatures. However, studies of social abnormalities in psychiatric conditions are often challenged by the difficulties of formalizing dynamic social exchanges and quantifying their neurocognitive underpinnings. Recently, the rapid growth of computational psychiatry as a new field along with the development of multiplayer economic paradigms provide powerful tools to parameterize complex interpersonal processes and identify quantitative indicators of social impairments. By utilizing these methodologies, the current set of studies aimed to examine social decision making during multiplayer economic games in participants diagnosed with depression (study 1) and combat-related post-traumatic stress disorder (PTSD, study 2), as well as an online population with elevated symptoms of borderline personality disorder (BPD, study 3). We then quantified and disentangled the impacts of multiple latent decision-making components, mainly social valuation and social learning, on maladaptive social behavior via explanatory modeling. Different underlying alterations were revealed across diagnoses. Atypical social exchange in depression and BPD were found attributed to altered social valuation and social learning respectively, whereas both social valuation and social learning contributed to interpersonal dysfunction in PTSD. Additionally, model-derived indices of social abnormalities positively correlated with levels of symptom severity (study 1 and 2) and exhibited a longitudinal association with symptom change (study 1). Our findings provided mechanistic insights into interpersonal difficulties in psychiatric illnesses, and highlighted the importance of a computational understanding of social function which holds potential clinical implications in differential diagnosis and precise treatment.Doctor of Philosophy
People with psychiatric conditions often suffer from impaired social relationships due to an inability to engage in everyday social interactions. As different illnesses can sometimes produce the same symptoms, social impairment can also have different causes. For example, individuals who constantly avoid social activities may find them less interesting or attempt to avoid potential negative experiences. While those who display elevated aggression may have a strong desire for social dominance or falsely believe that others are also aggressive. However, it is hard to infer what drives these alterations by just observing the behavior. To address this question, we enrolled people with three different kinds of psychopathology to play an interactive game together with another player and mathematically modeled their latent decision-making processes. By comparing their model parameters to those of the control population, we were able to infer how people with psychopathology made the decisions and which part of the decision-making processes went wrong that led to disrupted social interactions. We found altered model parameters differed among people with major depression, post-traumatic stress disorder and borderline personality disorder, suggesting different causes underlying impaired social behavior observed in the game, the extent of which also positively correlated with their psychiatric symptom severity. Understanding the reasons behind social dysfunctions associated with psychiatric illnesses can help us better differentiate people with different diagnoses and design more effective treatments to restore interpersonal relationships.
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Walshe, Ross Calen. "Operation of eye-movement control mechanisms during the perception of naturalistic scenes." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20966.
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Understanding of visual scenes takes place within very brief episodes known as fixations. To explore the extent of the scene, the eye shifts between fixation locations at intervals of roughly 300 ms. Currently, it is a matter of open inquiry as to what factors influence the timing of these movements. This thesis focuses on understanding the mechanisms that govern the rapid adjustment of fixation and saccade timings when novel stimulus information is encountered during a fixation. In part I, I use an experimental technique known as the fixation-contingent scene quality paradigm to control the quality of incoming visual scene information. This approach is used to assess how fixation timing adapts to moment-by-moment changes in the quality level of the stimulus. I find that quality changes tend to result in an increase in fixation durations and this occurs whether the quality is increased or decreased. Using distributional analytic techniques, I argue that these results reflect the combined influence of a rapid surprise related process and a slower acting encoding related influence. In part II, I study how fixation durations are influenced by the underlying saccade programming mechanisms. An important assumption within the eye-movement control literature is that there exists a threshold called the point-of-no-return. Once this point has been reached, a saccade may no longer be modified or cancelled. I adapt a classic psychophysical technique known as the double-step procedure to study the point-of-no-return within scene viewing tasks. I also provide a measurement of the saccadic dead time, the last point in time that a saccade may be modified. In Part III, a formal model of fixation durations in high-level tasks is presented. I build on recent modelling work and develop a formal account for the early-surprise late-encoding modulation account of fixation durations in scene viewing tasks. The model is tested against data observed in Part I of the thesis. I demonstrate that the model does a very good job of predicting these distributions with relatively few assumptions. In summary, I use experimental techniques in combination with computational modelling to reveal how a composite of low-level (saccade programming) and high-level (information processing) considerations can, and must, be taken into consideration when understanding eye-movement control behaviour in scene viewing tasks.
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Amanda, Tholin. "Utvecklingsmöjligheter vid användandet av making i programmeringsundervisning : En studie om elevers möjligheter och svårigheter i skapandet av kod." Thesis, Jönköping University, Högskolan för lärande och kommunikation, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-49215.
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Programming is a new subject in the Swedish curriculum, as a part in teaching mathematics. Literature studies highlights the need of empirical studies in order to develop computational thinking. “Making” is a pedagogical practice used in teaching programming which, according to previous research, has been successful with the aim of creating code in programming environments. However, there is critical opinions about the use of making and how the use of the practice can enable computational thinking. The purpose has therefore been to investigate the opportunities and difficulties that may arise in the use of making, as well as what active actions students choose to do when they encounter “bugs” when creating code. The aim of the study is therefore to investigate and deepen the understanding of the use to develop computational thinking in programming environments in later years mathematic education. In order to achieve the purpose of the study, participant observations were used as method in two classes in grades 7. Centrally for the method is that the observation amplifies with interviews. The result provides a basis for data which resulted in three subcategories in the material analysis, based on the theoretical framework of the study, which is computational thinking. The categories were named ”everyday examples”, “mathematics and programming syntax” and “endurance” (when student encounter “bugs”). The categories provided a basis for the opportunities and difficulties that can arise when using making and how teachers are supposed to teach to motivate students during bugs while creating code to develop computational thinking.
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Liu, Qingfang. "Dynamics of Multi-attribute Decision Making Revealed by Eye-tracking." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1609933430042674.
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Coppola, Emery A. "Optimal pumping policy for a public supply wellfield using computational neural network with decision-making methodology." Thesis, The University of Arizona, 2000. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu_e9791_2000_395_sip1_w.pdf&type=application/pdf.
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Coppola, Emery Albert. "Optimal pumping policy for a public supply wellfield using computational neural network with decision-making methodology." Diss., The University of Arizona, 2000. http://hdl.handle.net/10150/279840.
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Effective management of groundwater resources is at the forefront of environmental challenges confronting mankind in the 21st century. In many regions of the world, growing human populations coupled with decades of improper use and disposal of chemical contaminants have diminished the quantity and quality of this irreplaceable resource. It is projected that by the year 2025, thirty-five percent of the world population will experience chronic water shortages [Ahfeld et al, 2000]. As much of the world relies upon groundwater as its drinking water source (e.g. 51.7% of the United States population), optimal management will become increasingly important. Complicating the problem is that human use considerations must be balanced with environmental and economic concerns. Balancing multiple concerns such as these constitutes a multiobjective and conflict resolution problem, where tradeoffs among non-commensurable objectives must be identified to select the best compromise solution. In this research, a Computational Neural Network (CNN) methodology has been developed for identifying pumping policies for public supply wells that effectively balance risk of contamination with supply objectives. Utilizing simulation results from MODFLOW, monthly CNN's were developed to predict groundwater elevations at select locations for a hypothetical but realistic unconfined, heterogeneous aquifer under variable monthly pumping and recharge rates. The resulting CNN architecture, a simplified linear approximation to the finite-difference flow equations, was embedded into a linear optimization program, and an objective function that quantified both risk and supply was solved for using different weight preferences. The resulting Pareto frontier served as the basis for multiobjective and conflict resolution analyses. The CNN and decision-making methodologies were then applied to a real-world test case in Toms River, New Jersey, where contaminated public supply wells, a suspected cancer cluster, and few alternative water sources motivated the need for a formal and rigorous analysis that identified the best compromise solution. The new CNN methodology achieved a very high degree of accuracy in both simulation and optimization, and, once trained, is computationally more efficient than traditional methods. Perhaps most importantly, this research demonstrates the theoretical possibility of training a CNN with real-world data, allowing direct optimization of the actual groundwater system.
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Kiss, Tamas. "Making distributed computing infrastructures interoperable and accessible for e-scientists at the level of computational workflows." Thesis, University of Westminster, 2012. https://westminsterresearch.westminster.ac.uk/item/8z351/making-distributed-computing-infrastructures-interoperable-and-accessible-for-e-scientists-at-the-level-of-computational-workflows.
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As distributed computing infrastructures evolve, and as their take up by user communities is growing, the importance of making different types of infrastructures based on a heterogeneous set of middleware interoperable is becoming crucial. This PhD submission, based on twenty scientific publications, presents a unique solution to the challenge of the seamless interoperation of distributed computing infrastructures at the level of workflows. The submission investigates workflow level interoperation inside a particular workflow system (intra-workflow interoperation), and also between different workflow solutions (inter-workflow interoperation). In both cases the interoperation of workflow component execution and the feeding of data into these components workflow components are considered. The invented and developed framework enables the execution of legacy applications and grid jobs and services on multiple grid systems, the feeding of data from heterogeneous file and data storage solutions to these workflow components, and the embedding of non-native workflows to a hosting meta-workflow. Moreover, the solution provides a high level user interface that enables e-scientist end-users to conveniently access the interoperable grid solutions without requiring them to study or understand the technical details of the underlying infrastructure. The candidate has also developed an application porting methodology that enables the systematic porting of applications to interoperable and interconnected grid infrastructures, and facilitates the exploitation of the above technical framework.
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Fröcklin, Henry. "Computational model for morality and emotions in EmoBN." Thesis, Linköpings universitet, Medie- och Informationsteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-112096.
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This master thesis presents an approach on how to design moral behaviour in a scenario with een. een is an iteration of emobn which is based on bn, an action selection system with activation dynamics among modules, goal oriented and capable of prediction and planing. The design is based on current research from prominent psychologist like Haidt and uses Mikhial’s umg framework for causal and intentional validation. Also Roseman’s appraisal model and Haidt’s mft is used for determining moral emotions in a moral context. The design is tested against empirical results from philosophical experiment know as the trol- ley problem, a well known moral dilemma.
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Sebold, Miriam Hannah. "From Goals to Habits in Alcohol Dependence: Psychological and Computational Investigations." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18066.
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Alkoholabhängigkeit (AA) zeichnet sich durch einen starken Drang nach Alkoholkonsum trotz schwerwiegender negativer Folgen aus. Eine gängige Theorie aus der Suchtforschung besagt, dass AA mit einer Verlagerung von zielgerichteter zu habitueller Kontrolle einhergeht, durch welche Handlungen automatisiert ausgeführt werden und weitgehend unabhängig von ihren Folgen sind. Evidenzen hierfür stammen weitgehend aus experimentellen Untersuchungen an Tieren.
Das Fachgebiet des maschinellen Lernens hat zudem neue Experimente hervorgebracht, welche die Anwendung von Algorithmen erlauben, um die Verlagerung von zielgerichtetem zu habituellen Verhalten zu untersuchen. Diese Paradigmen fanden bisher keine Anwendung in der Untersuchung von alkoholabhängigen Patienten.
Daher widmet sich diese Dissertation der Untersuchung von habituellem und zielgerichtetem Verhalten bei AA aus unterschiedlichen Perspektiven. Hierfür adaptierten wir zunächst ein Paradigma aus der Tierliteratur, durch welches habituelles Verhalten als reizgesteuerte Kontrolle quantifiziert wird. Anschließend nutzten wir eine Aufgabe, die aus dem maschinellen Lernen stammt und die Untersuchung von habitueller und zielgerichteter Kontrolle ermöglicht. Drittens untersuchten wir den Zusammenhang des Verhaltens über beiden Paradigmen hinweg. Zuletzt untersuchten wir, ob habitueller und zielgerichteter Kontrolle mit dem Alkoholkonsum in jungen sozialen Trinkern assoziiert ist.
Die Ergebnisse liefern weitere Hinweise auf eine Verlagerung von zielgerichteter zu habitueller Kontrolle bei AA. Das Verhalten in beiden Paradigmen war miteinander assoziiert, was darauf rückschließen lässt, dass ähnliche kognitive Mechanismen involviert sind. Soziale Trinker zeigten keine Verlagerung von zielgerichteter zu habituellem Verhalten, was darauf hin weist, dass jenes Ungleichgewicht erst im Verlauf der AA entsteht und kein Korrelat von Alkoholkonsum per se darstellt.Alcohol dependence (AD) manifests as a strong drive to consume alcohol despite serious adverse consequences. A popular theory in addiction research thus suggests that AD is characterized by a shift from goal-directed to habitual control, where actions are automatic and disentangled from outcomes. Evidence for this has mainly been drawn from experimental investigations in animals. The field of machine learning has additionally advanced new experiments that allow the application of reinforcement learning algorithms to investigate a shift towards habits. Again, these tasks have yet not been applied to human AD. To fill this gap, this thesis investigates habitual at the expense of goal-directed control from distinct theoretical fields in AD patients. We adapted a paradigm from the animal literature, which quantifies habits as cue-induced control over behavior. Then, we applied an experimental procedure inspired from machine learning that allows to investigate the balance between habitual and goal-directed control. Third, we examined the relationship between behavior across these paradigms. Last, we investigated whether the imbalance between habitual and goal-directed control was associated with alcohol consumption in young social drinkers. Our results add further evidence that AD is associated with a shift from goal-directed to habitual control, e.g. increased cue-induced control / reductions in goal-directed decision-making. Behavior across both paradigms were associated with each other, suggesting the involvement of similar mechanisms. As non-pathological alcohol intake was not associated with an imbalance between goal-directed and habitual control, this imbalance might arise over the course of AD rather than being a trait marker of alcohol intake.
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Markovic, Milan. "Utilising provenance to enhance social computation." Thesis, University of Aberdeen, 2016. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=228546.
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Many online platforms employ networks of human workers to perform computational tasks that can be difficult for a machine to perform (e.g. recognising an object from an image). This approach can be referred to as social computation. However, systems that utilise social computation often suffer from a lack of transparency, which results in difficulties in the decision-making process (e.g. assessing reliability of outputs). This thesis investigates how the lack of transparency can be addressed by recording provenance, which includes descriptions of social computation workflows and their executions. In addition, it investigates the role of Semantic Web technologies in modelling and querying such provenance in order to support decision-making. Following analysis of several use-case scenarios, requirements for describing the provenance of a social computation are identified to provide the basis of the Social Computation Provenance model, SC-PROV. This model extends the W3C recommendation for modelling provenance on the Web (PROV) and the P-PLAN model for describing provenance of abstract workflows. To satisfy the identified provenance requirements, SC-PROV extends PROV and P-PLAN with a vocabulary for capturing social computation features such as social actors (e.g. workers and requesters), incentives (e.g. promises of monetary rewards received upon completion of a task), and conditions (e.g. constraints defining when an incentive should be awarded). The SC-PROV model is realised in an OWL ontology and used in a semantic annotation framework to capture the provenance of a simulated case study, which includes 46,665 diverse workflows. During the evaluation process, the SC-PROV vocabulary is used to construct provenance queries that support an example workflow selection metric based on trust assessments of various aspects of social computation workflows. The performance of the workflow selected by this metric is then evaluated against the performance of two control groups - one containing randomly selected workflows and the other containing workflows selected by a metric informed by provenance which lacks SCPROV descriptions. The examples described in this thesis establish the benefits of examining provenance as part of decision-making in the social computation domain, and illustrate the inability of current provenance models to fully support these processes. The evaluation of SC-PROV demonstrates its capabilities to produce provenance descriptions that extend to the social computation domain. The empirical evidence provided by the evaluation supports the conclusion that using SC-PROV enhances support for trust-based decision-making.
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Antos, Dimitrios. "Deploying Affect-Inspired Mechanisms to Enhance Agent Decision-Making and Communication." Thesis, Harvard University, 2012. http://dissertations.umi.com/gsas.harvard:10107.
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Computer agents are required to make appropriate decisions quickly and efficiently. As the environments in which they act become increasingly complex, efficient decision-making becomes significantly more challenging. This thesis examines the positive ways in which human emotions influence people’s ability to make good decisions in complex, uncertain contexts, and develops computational analogues of these beneficial functions, demonstrating their usefulness in agent decision-making and communication. For decision-making by a single agent in large-scale environments with stochasticity and high uncertainty, the thesis presents GRUE (Goal Re-prioritization Using Emotion), a decision-making technique that deploys emotion-inspired computational operators to dynamically re-prioritize the agent’s goals. In two complex domains, GRUE is shown to result in improved agent performance over many existing techniques. Agents working in groups benefit from communicating and sharing information that would otherwise be unobservable. The thesis defines an affective signaling mechanism, inspired by the beneficial communicative functions of human emotion, that increases coordination. In two studies, agents using the mechanism are shown to make faster and more accurate inferences than agents that do not signal, resulting in improved performance. Moreover, affective signals confer performance increases equivalent to those achieved by broadcasting agents’ entire private state information. Emotions are also useful signals in agents’ interactions with people, influencing people’s perceptions of them. A computer-human negotiation study is presented, in which virtual agents expressed emotion. Agents whose emotion expressions matched their negotiation strategy were perceived as more trustworthy, and they were more likely to be selected for future interactions. In addition, to address similar limitations in strategic environments, this thesis uses the theory of reasoning patters in complex game-theoretic settings. An algorithm is presented that speeds up equilibrium computation in certain classes of games. For Bayesian games, with and without a common prior, the thesis also discusses a novel graphical formalism that allows agents’ possibly inconsistent beliefs to be succinctly represented, and for reasoning patterns to be defined in such games. Finally, the thesis presents a technique for generating advice from a game’s reasoning patterns for human decision-makers, and demonstrates empirically that such advice helps people make better decisions in a complex game.Engineering and Applied Sciences
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Martí, Ortega Daniel. "Neural stochastic dynamics of perceptual decision making." Doctoral thesis, Universitat Pompeu Fabra, 2008. http://hdl.handle.net/10803/7552.
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Models computacionals basats en xarxes a gran escala d'inspiració neurobiològica permeten descriure els correlats neurals de la decisió observats en certes àrees corticals com una transició entre atractors de la xarxa cortical. L'estimulació provoca un canvi en el paisatge d'atractors que afavoreix la transició entre l'atractor neutre inicial a un dels atractors associats a les eleccions categòriques. El soroll present en el sistema introdueix indeterminació en les transicions. En aquest treball mostrem l'existència de dos mecanismes de decisió qualitativament diferents, cadascun amb signatures psicofísiques diferenciades. El mecanisme que apareix a baixes intensitats, induït exclusivament pel soroll, dóna lloc a temps de decisió distribuïts asimètricament, amb una mitjana dictada per l'amplitud del soroll.A més, tant els temps de decisió com el rendiment psicofísic són funcions decreixents de l'estimulació externa. També proposem dos mètodes, un basat en l'aproximació macroscòpica i un altre en la teoria de la varietat central, que simplifiquen la descripció de sistemes estocàstics multistables.Computational models based on large-scale, neurobiologically-inspired networks describe the decision-related activity observed in some cortical areas as a transition between attractors of the cortical network. Stimulation induces a change in the attractor configuration and drives the system out from its initial resting attractor to one of the existing attractors associated with the categorical choices. The noise present in the system renders transitions random. We show that there exist two qualitatively different mechanisms for decision, each with distinctive psychophysical signatures. The decision mechanism arising at low inputs, entirely driven by noise, leads to skewed distributions of decision times, with a mean governed by the amplitude of the noise. Moreover, both decision times and performances are monotonically decreasing functions of the overall external stimulation. We also propose two methods, one based on the macroscopic approximation and one based on center manifold theory, to simplify the description of multistable stochastic neural systems.
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Michael, Elizabeth. "Dissociable sources of uncertainty in perceptual decision making." Thesis, University of Oxford, 2016. http://ora.ox.ac.uk/objects/uuid:581e8fc9-1e12-4877-a89a-44cdc67c45e2.
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The natural world provides sensory systems with noisy and ambiguous information, which is often transformed into a more stable categorical percept. This thesis aims to investigate the nature of the neural representations in the visual system that support this transformation. To do so, we will employ a behavioural task that requires participants to average several independent sources of perceptual information. This task allows for the dissociation of two theoretically orthogonal sources of decision uncertainty: the mean distance of the perceptual information from a category boundary and the variability of the evidence under consideration. Behaviourally, both decreasing the mean distance to bound of information and increasing information variability are associated with increased errors and prolonged response times. We will present a computational model that can account for the independent behavioural effects of these two sources of uncertainty by assuming that categorical decisions are made on the basis of a probabilistic transformation of perceptual evidence. BOLD measurements demonstrate that these effects of mean and variability are supported by a partially dissociable network of brain regions. Electroencephalography demonstrates the differential influence of mean and variance in the pre- and post-decision period. Furthermore, we show that there is adaptation at the level of the perceptual representation to the information variance. Not only does this show that the visual system must represent information at the summary level, in addition to individual feature-based representation, but it also suggests that the costs associated with this form of perceptual uncertainty can be largely mitigated by the adoption of a more suitable representational range.
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Horvath, Lilla [Verfasser]. "Computational characterization of human sequential decision making under uncertainty : Model-free, model-based, exploitative and explorative strategies / Lilla Horvath." Berlin : Freie Universität Berlin, 2021. http://d-nb.info/1237685915/34.
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Atwell, Kathryn. "Investigating the interplay between cellular mechanics and decision-making in the C. elegans germ line." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:a641df49-c050-460a-bda5-7231d6fa67ad.
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The behaviour of individual cells must be carefully coordinated across a tissue to achieve correct function. In particular, proliferation and differentiation decisions must be precisely regulated throughout development, tissue maintenance, and repair. A better understanding of how these processes are controlled would have implications for human health; cancer is, after all, dysregulated proliferation, while regenerative medicine relies on being able to influence cell decisions accurately. To investigate such fundamental biological processes, it is common practice to use an experimentally tractable model organism. Here, we focus on the germ line of the nematode worm C. elegans, which provides opportunities to study organogenesis, tissue maintenance, and ageing effects. Despite the advantages of this organism as a biological model, certain questions about germ cell behaviour and coordination remain challenging to address in the lab. There is therefore a need for computational models of the germ line to complement experimental approaches. In this thesis, we develop a new in silico model of the C. elegans germ line. Novel aspects include working in three dimensions, covering the late larval period, and integrating a logical model of germ cell behaviour into a wider cell mechanics simulation. Our model produces a reasonable fit to wild-type germline behaviour, and provides the first cell tracking and labelling predictions for the larval period. It also suggests two new biological hypotheses: 1) that âstretchingâ growth plays a significant role in gonadogenesis, and 2) that a feedback mechanism acts on the germ cell cycle to prevent overproliferation. Having introduced the full model, we address some technical questions arising from our work, namely: what is the effect of applying a more physically realistic force law?; and can simulation performance be improved by changing the numerical scheme? Finally, we use in silico modelling to compare a number of hypothesised germ line maintenance mechanisms. There, our results support a model with functionally equivalent germ cells undergoing at most infrequent, transient cell cycle arrests.
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Li, Xiaofei. "Dynamic Goal Choice when Environment Demands Exceed Individual’s Capacity: Scaling up the Multiple-Goal Pursuit Model." Ohio University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1493389920717575.
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Desai, Nitisha. "Modeling Biases in Value-Based Decisions." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1555514474952847.
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Bolenz, Florian, Andrea M. F. Reiter, and Ben Eppinger. "Developmental Changes in Learning: Computational Mechanisms and Social Influences." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2018. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-232296.
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Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.
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Bolenz, Florian, Andrea M. F. Reiter, and Ben Eppinger. "Developmental Changes in Learning: Computational Mechanisms and Social Influences." Frontiers Research Foundation, 2017. https://tud.qucosa.de/id/qucosa%3A30736.
Full textAbstract:
Our ability to learn from the outcomes of our actions and to adapt our decisions accordingly changes over the course of the human lifespan. In recent years, there has been an increasing interest in using computational models to understand developmental changes in learning and decision-making. Moreover, extensions of these models are currently applied to study socio-emotional influences on learning in different age groups, a topic that is of great relevance for applications in education and health psychology. In this article, we aim to provide an introduction to basic ideas underlying computational models of reinforcement learning and focus on parameters and model variants that might be of interest to developmental scientists. We then highlight recent attempts to use reinforcement learning models to study the influence of social information on learning across development. The aim of this review is to illustrate how computational models can be applied in developmental science, what they can add to our understanding of developmental mechanisms and how they can be used to bridge the gap between psychological and neurobiological theories of development.
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Schärfe, Charlotta Pauline Irmgard [Verfasser], and Oliver [Akademischer Betreuer] Kohlbacher. "Towards Personalized Medicine : Computational Approaches to Support Drug Design and Clinical Decision Making / Charlotta Pauline Irmgard Schärfe ; Betreuer: Oliver Kohlbacher." Tübingen : Universitätsbibliothek Tübingen, 2019. http://d-nb.info/1176510053/34.
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Cogliati, Dezza Irene. "“Vanilla, Vanilla .but what about Pistachio?” A Computational Cognitive Clinical Neuroscience Approach to the Exploration-Exploitation Dilemma." Doctoral thesis, Universite Libre de Bruxelles, 2018. https://dipot.ulb.ac.be/dspace/bitstream/2013/278730/3/Document1.pdf.
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On the 24th November of 1859, Charles Darwin published the first edition of The Origin of Species. One hundred fifty-nine years later, our understanding of human and animal adaptation to the surrounding environment remains a major scientific challenge. How do humans and animals generate apt decision strategies in order to achieve this adaptation? How does their brain efficiently carry out complex computations in order to produce such adaptive behaviors? Although an exhaustive answer to these questions continues to feel out of reach, the investigation of adaptive processing results relevant in understanding mind/brain relationship and in elucidating scenarios where mind/brain interactions are corrupted such as in psychiatric disorders. Additionally, understanding how the brain efficiently scales problems when producing complex and adaptive behaviors can inspire and contribute to resolve Artificial Intelligence (AI) problems (e.g. scaling problems, generalization etc.) and consequently to the develop intelligent machines. During my PhD, I investigated adaptive behaviors at behavioral, cognitive, and neural level. I strongly believe that, as Marr already pointed out, in order to understand how our brain-machine works we need to investigate the phenomenon from 3 different levels: behavioral, algorithm and neural implementation. For this reason, throughout my doctoral work I took advantages of computational modeling methods together with cognitive neuroscience techniques in order to investigate the underlying mechanisms of adaptive behaviors.Doctorat en Sciences psychologiques et de l'éducation
info:eu-repo/semantics/nonPublished
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Busch, Julia Maria Christiane. "The making and breaking of SAS-6 : structural insights and inhibitor search for n-terminal domain dimerisation." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:2d5e4713-e645-40e9-87a1-88a7425d93eb.
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SAS-6 is the structural core of the forming centriole - a cylindrical protein complex, which is an essential component of the centrosome. Oligomerisation of SAS-6 is crucial for successful centriole duplication and is achieved through two dimerisation domains in the SAS-6 protein; a long C-terminal coiled-coil domain and a globular N-terminal dimerisation domain. As core components of the centrosome, centrioles help facilitate various cellular functions. They are involved in the anchoring of flagella and cilia to the membrane and in coordinating the spindle apparatus during chromosome segregation. A deeper insight into the molecular mechanisms at play in the centriole duplication process would have implications on our understanding of fundamental cell division processes and a number of related diseases. Here the involvement of an unstudied loop region in the C. elegans SAS-6 N-terminal domain dimerisation is described. Combining structural biology, biophysical and computational techniques, the molecular interactions of this loop were explored, contributing to the oligomerisation of SAS-6 at the N-terminal dimer interface. Furthermore, the screening and testing of small molecule inhibitors of the SAS-6 N-terminal domain dimerisation is described, targeting a hydrophobic pocket in the domain. Two candidate compounds are presented as a result of the screens and next steps towards structure based compound design are suggested, based on computational analysis. The search for inhibitory compounds includes a set-up of an in-house virtual screening pipeline, as well as in vitro screening efforts and a new crystallographic structure of the H. sapiens SAS-6 N-terminal domain. By investigating the making and breaking of the SAS-6 N-terminal domain dimerisation, light is shed on so far neglected details of this essential protein-protein interaction and advancements towards a SAS-6 oligomerisation inhibitor described, which could ultimately be used for new approaches in cell cycle research and might open up new avenues for medical research by binding a disease relevant target.
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Banks, Gatenby Amanda. "Developing perspectives of knowledgeability through a pedagogy of expressibility with the Raspberry Pi." Thesis, University of Manchester, 2018. https://www.research.manchester.ac.uk/portal/en/theses/developing-perspectives-of-knowledgeability-through-a-pedagogy-of-expressibility-with-the-raspberry-pi(246a7889-d2a5-41ad-bd15-e04c0f36b529).html.
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The curriculum for ICT in UK schools was discontinued in September 2012 and replaced by a 'rebranded' subject of Computing, divided into three sub domains: Computer Science; Information Technology; and digital literacy. The latter was positioned as basic technical skills. There were concerns in the education community that the new curriculum promoted programming and computer science topics to the detriment of digital literacy and applied uses of technology. Much of the Computing education literature perpetuates the hegemony of the logical and abstract, and implies computational thinking and rationality are synonymous with criticality. During the same period, a maker culture was growing rapidly in the UK, and discourses around these activities promoted an entirely different notion of digital literacy, aligned with the wide body of literacy literature that focuses on notions of empowerment and criticality rather than basic functional skills. A digital maker tool called the Raspberry Pi was released with the intention of supporting the development of computer science and digital making competence, and thus sat at the boundary of the academic and maker communities. This thesis argues that developing 'criticality' is a vital component of Computing education and explores how learning activities with the Raspberry Pi might support development of 'criticality'. In setting the scene for the investigation, I will first explore the notions underpinning discourse around both computational and critical thinking and digital literacy, suggesting that the frictions would be best overcome by abandoning abstract constructs of knowledge and assumptions that it is possible to separate theory and practice. I show how the term 'critical' is itself problematic in the literature and I look to Wenger's social theory of learning to avoid the individualistic limits of Papert's constructionism, a popular learning theory in Computing education. Wenger's constructs of knowledgeability and competence help tell a different story of what it means to be a learner of the practice of Computing, both in learning for academic purposes and with intentions towards becoming a practitioner. In concert with learning citizenship, these constructs offer a more ethical framing of 'criticality'. Informed by this theoretical position, I suggest an original, exploratory implementation of Q methodology to explore learning with technology in school settings. I qualitatively compare 'before' and 'after' Q studies that represent perspectives at the individual and collective level, with reference to observations of classroom learning. The methodology facilitates a nuanced and complex investigation and the findings of the project suggest that where pupils are already predisposed to the subject, working with the Raspberry Pi develops a broader knowledgeability, but where there is no such predisposition, a pedagogy of expressibility influences how participation in Raspberry Pi learning activities may impact knowledgeability.
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Tommasi, Ilaria. "Branch Making Shells: applicazione di sistemi multi-agente alla formazione di strutture monosuperficie con topologia ramificata." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23407/.
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Con questa tesi si studiano le potenzialità di un agent-system per la generazione di strutture monosuperficie con topologia ramificata, con l’intento di generare architetture inseribili in contesti esistenti. Gli agenti agiscono leggendo informazioni date dall’ambiente e dal sistema stesso, adattandovi parametri di comportamento.
Il sistema creato parte dalle logiche dell’algoritmo di Space Colonization (utilizzato per simulare la crescita di strutture ramificate presenti in natura), basandosi quindi su punti attrattori nello spazio che guidano lo sviluppo di ramificazioni.
Lo studio si è concentrato sull’applicazione del sistema lungo superfici modellate sfruttando le potenzialità del territorio. L’analisi strutturale di queste, fornisce dati che influenzano la distribuzione dei punti, quindi lo sviluppo e la variabilità del pattern (direzionalità, frequenza e angolo di ramificazione) in funzione delle logiche costruttive proposte.
La complessità e variabilità del pattern generato, applicato a superfici a doppia curvatura non consente una standardizzazione degli elementi costruttivi: sono perciò state prese in considerazione diverse possibilità, cercando compromessi tra semplicità esecutiva ed estetica. Si è scelto un sistema materiale che lavora per elementi discreti, traducendo le tracce digitali in una tettonica organizzata in elementi curvi in legno collegati ai nodi con pezzi metallici.
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Valton, Vincent. "Impaired reinforcement learning and Bayesian inference in psychiatric disorders : from maladaptive decision making to psychosis in schizophrenia." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/21097.
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Computational modelling has been gaining an increasing amount of support from the neuroscience community as a tool to assay cognition and computational processes in the brain. Lately, scientists have started to apply computational methods from neuroscience to the study of psychiatry to gain further insight into the mechanisms leading to mental disorders. In fact, only recently has psychiatry started to move away from categorising illnesses using behavioural symptoms in an attempt for a more biologically driven diagnosis. To date, several neurobiological anomalies have been found in schizophrenia and led to a multitude of conceptual framework attempting to link the biology to the patients’ symptoms. Computational modelling can be applied to formalise these conceptual frameworks in an effort to test the validity or likelihood of each hypothesis. Recently, a novel conceptual model has been proposed to describe how positive symptoms (delusions, hallucinations and thought disorder) and cognitive symptoms (poor decision-making, i.e. “executive functioning”) might arise in schizophrenia. This framework however, has not been tested experimentally or against computational models. The focus of this thesis was to use a combination of behavioural experiments and computational models to independently assess the validity of each component that make up this framework. The first study of this thesis focused on the computational analysis of a disrupted prediction-error signalling and its implications for decision-making performances in complex tasks. Briefly, we used a reinforcement-learning model of a gambling task in rodents and disrupted the prediction-error signal known to be critical for learning. We found that this disruption can account for poor performances in decision-making due to an incorrect acquisition of the model of the world. This study illustrates how disruptions in prediction-error signalling (known to be present in schizophrenia) can lead to the acquisition of an incorrect world model which can lead to poor executive functioning or false beliefs (delusions) as seen in patients. The second study presented in this thesis addressed spatial working memory performances in chronic schizophrenia, bipolar disorder, first episode psychosis and family relatives of DISC1 translocation carriers. We build a probabilistic inference model to solve the working memory task optimally and then implemented various alterations of this model to test commonly debated hypotheses of cognitive deficiency in schizophrenia. Our goal was to find which of these hypotheses accounts best for the poor performance observed in patients. We found that while the performance at the task was significantly different for most patients groups in comparison to controls, this effect disappeared after controlling for IQ in one group. The models were nonetheless fitted to the experimental data and suggest that working memory maintenance is most likely to account for the poor performances observed in patients. We propose that the maintenance of information in working memory might have indirect implications for measures of general cognitive performance, as these rely on a correct filtering of information against distractions and cortical noise. Finally the third study presented in this thesis assessed the performance of medicated chronic schizophrenia patients in a statistical learning task of visual stimuli and measured how the acquired statistics influenced their perception. We find that patient with chronic schizophrenia appear to be unimpaired at statistical learning of visual stimuli. The acquired statistics however appear to induce less expectation-driven ‘hallucinations’ of the stimuli in the patients group than in controls. We find that this is in line with previous literature showing that patients are less susceptible to expectation-driven illusions than controls. This study highlights however the idea that perceptual processes during sensory integration diverge from this of healthy controls. In conclusion, this thesis suggests that impairments in reinforcement learning and Bayesian inference appear to be able to account for the positive and cognitive symptoms observed in schizophrenia, but that further work is required to merge these findings. Specifically, while our studies addressed individual components such as associative learning, working memory, implicit learning & perceptual inference, we cannot conclude that deficits of reinforcement learning and Bayesian inference can collectively account for symptoms in schizophrenia. We argue however that the studies presented in this thesis provided evidence that impairments of reinforcement learning and Bayesian inference are compatible with the emergence of positive and cognitive symptoms in schizophrenia.
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Marcos, Sanmartín Encarni. "Embodied decision making and its neural substrate." Doctoral thesis, Universitat Pompeu Fabra, 2014. http://hdl.handle.net/10803/285379.
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Decisions are the result of a deliberative process that evaluates the suitability of specific options. Studies about decision making have been mainly conducted by using restricted tasks in which humans or animals are requested to discriminate between options. However, the influence that factors related to embodiment, such as motor cost, might have on this process has frequently been ignored. In this thesis, we adopt a combined experimental and theoretical approach to examine the effect that such factors have on decision making. Our results confirm an important bias of behavior and neural activity resulting from factors related to embodiment that are external to the goal of the task itself. We use computational models to account for this bias and to shed some light on the neural mechanisms producing it. Our results translate into significant progress in the understanding of embodied decision making, providing new insights into neural mechanisms and theoretical models.Las decisiones son el resultado de un proceso de deliberación que evalúa la idoneidad de opciones específicas. Los estudios acerca de la toma de decisiones han estado principalmente dirigidos usando tareas restringidas en las que a los humanos o animales se les pide escoger entre opciones. Sin embargo, la influencia que factores relacionados con la corporificación de la toma de decisiones podrían tener en este proceso se ha ignorado frecuentemente. En esta tesis, adoptamos un enfoque experimental y teórico combinado para examinar la influencia que estos factores tienen en la toma de decisiones. Nuestros resultados confirman un importante sesgado del comportamiento y de la actividad neuronal causados por factores que son externos al objetivo de la tarea en sí. Utilizamos modelos computacionales para interpretar este sesgado que, a su vez, nos da una intuición del mecanismo neuronal que los está produciendo. Nuestros resultados se traducen en un significante progreso en la comprensión de la toma de decisiones corporificada, aportando nuevos conocimientos sobre los mecanismos neuronales y modelos teóricos.
Les decisions són el resultat d'un procés de deliberació que avalua la idoneïtat d'opcions específiques. Els estudis sobre la presa de decisions han estat principalment dirigits fent servir tasques restringides a les quals, als humans o animals, se'ls demana escollir entre opcions. No obstant, la influència que factors relacionats amb la corporificació de la presa de decisions podrien tenir en aquest procés s'ha ignorat freqüentment. En aquesta tesi, adoptem un enfocament experimental i teòric combinat per tal d'examinar la influència que aquests factors tenen en la presa de decisions. Els nostres resultats confirmen un important esbiaixat del comportament i de l'activitat neuronal degut a factors externs a l'objectiu de la tasca en sí. Fem servir models computacionals per tal d'interpretar aquest esbiaixat que, a la vegada, ens dóna una intuïció del mecanisme que l'està produint. La tesi conclou amb la presentació d'un únic model que integra tots els descobriments presentats i que podria utilitzar-se com a nou marc teòric per a recerques futures. En general, els resultats inclosos aquí es tradueixen en un significant progrés a la comprensió de la presa de decisions corporificada, aportant nous coneixements sobre els mecanismes neuronals i models teòrics.
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Cuevas, Rivera Dario [Verfasser], Stefan [Akademischer Betreuer] Kiebel, Stefan [Gutachter] Kiebel, and Michael [Gutachter] Smolka. "Dynamic computational models of risk and effort discounting in sequential decision making / Dario Cuevas Rivera ; Gutachter: Stefan Kiebel, Michael Smolka ; Betreuer: Stefan Kiebel." Dresden : Technische Universität Dresden, 2021. http://d-nb.info/1236384024/34.
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