Thesis (M. Phil.)--University of Hong Kong, 1994. / Includes bibliographical references (leaf 162-170).

Thesis (Ph. D.)--Oregon State University, 2007. / Printout. Includes bibliographical references (leaves 180-185). Also available on the World Wide Web.

No description available.

In many settings, agents engage in problem-solving activities, which require them to share resources, act on each others behalf, coordinate individual acts, etc. If autonomous agents are to e ectively interact (or support interaction among humans) in situations such as deciding whom and how to approach the provision of a resource or the performance of an action, there are a number of important questions to address. Who do I choose to delegate a task to? What do I need to say to convince him/her to do something? Were similar requests granted from similar agents in similar circumstances? What arguments were most persuasive? What are the costs involved in putting certain arguments forward? Research in argumentation strategies has received signi cant attention in recent years, and a number of approaches has been proposed to enable agents to reason about arguments to present in order to persuade another. However, current approaches do not adequately address situations where agents may be operating under social constraints (e.g., policies) that regulate behaviour in a society. In this thesis, we propose a novel combination of techniques that takes into consideration the policies that others may be operating with. First, we present an approach where evidence derived from dialogue is utilised to learn the policies of others. We show that this approach enables agents to build more accurate and stable models of others more rapidly. Secondly, we present an agent decision-making mechanism where models of others are used to guide future argumentation strategy. This approach takes into account the learned policy constraints of others, the cost of revealing in- formation, and anticipated resource availability in deciding whom to approach. We empirically evaluate our approach within a simulated multi-agent frame- work, and demonstrate that through the use of informed strategies agents can improve their performance.

Teleological reasoning reflects the general tendency to view objects, behaviors and events in terms of their “purpose.” Although healthy educated adults tend to refrain from committing errors in teleological reasoning about objects, our knowledge regarding how adults reason about events is limited. It has been suggested that teleological reasoning biases our interpretations of emotionally significant and unexpected life events of which a physical or social cause is absent or unsatisfactory. The current investigation seeks to better understand the types of events that evoke a teleological perspective and the conditions and individual difference factors that facilitate it. The results revealed that participants high in religiosity and low in ACT science reasoning are more likely to commit teleological errors (i.e., imbuing purpose upon events with non-intentional causal forces). Additionally, participants of low religiosity were more likely to commit teleological errors when placed under cognitive load. It appears that two routes to teleological reasoning exist: one that represents an explicit belief system such as religion, and one that reflects implicit intuitions about how the world works. These findings shed light on how, when confronted with certain life events, both our belief systems and situational pressures lead us to rely on intuitive assumptions rather than engage in careful consideration of more scientifically sound alternatives.

Submitted by Marlene Santos (marlene.bc.ufg@gmail.com) on 2017-08-22T16:51:39Z No. of bitstreams: 2 Dissertação - Ana Clara Araújo Gomes da Silva - 2017.pdf: 4600211 bytes, checksum: 1db7798a4333fe327ab9c0b9b4889c48 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Approved for entry into archive by Cláudia Bueno (claudiamoura18@gmail.com) on 2017-09-04T19:53:16Z (GMT) No. of bitstreams: 2 Dissertação - Ana Clara Araújo Gomes da Silva - 2017.pdf: 4600211 bytes, checksum: 1db7798a4333fe327ab9c0b9b4889c48 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2017-09-04T19:53:17Z (GMT). No. of bitstreams: 2 Dissertação - Ana Clara Araújo Gomes da Silva - 2017.pdf: 4600211 bytes, checksum: 1db7798a4333fe327ab9c0b9b4889c48 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2017-08-17 / The deficiency in some reasoning patterns of students has been measured and revealed by several studies. This deficiency is often reflected in low rates of academic performance, high dropout rates, and negatively influencing students’ interest in computer courses. In recent work, Pessoni [96] has shown that among more than thirty cognitive development assessment methods studied, Lawson’s Classroom Test of Scientific Reasoning (LCTSR) would be a candidate for application in university students according to the criteria defined. The LCTSR was translated and applied in a series of student groups of the undergraduate courses of the Institute of Informatics of the Federal University of Goiás. Starting from the results obtained by Pessoni, the objectives of this research work are: 1. Systematic Review of Literature - RSL; 2. Application of the Scientific Reasoning Examination - ERC in students of Computing in Southwest Goiania; 3. Phylogenetic systematics to evaluate ERC. Analyze the patterns of reasoning of interest groups, then develope and conduct efficient and effective activities that promote the cognitive development of individuals is one of the objectives of the research group. It is believed that this work contributes to a further step in this direction. / A deficiência em alguns padrões de raciocínio dos estudantes tem sido medida e revelada por diversos estudos. Essa deficiência muitas vezes se reflete em baixas taxas de rendimento, altas taxas de evasão e influenciam negativamente no interesse dos discentes pelos cursos da Computação. Em trabalho recente, Pessoni [96] mostrou que, entre mais de trinta métodos de avaliação do desenvolvimento cognitivo estudados, o Lawson’s Classroom Test of Scientific Reasoning (LCTSR) seria um candidato para aplicação em estudantes universitários segundo os critérios definidos. O LCTSR foi traduzido e aplicado em uma série de grupos de estudantes dos cursos de graduação do Instituto de Informática da Universidade Federal de Goiás. Partindo dos resultados obtidos por Pessoni, este trabalho de pesquisa teve como objetivos: 1. Revisão Sistemática da Literatura - RSL; 2. aplicação do Exame do Raciocínio Científico - ERC em estudantes de Computação do Sudoeste Goiano; 3. Sistemática Filogenética para avaliar o ERC. Analisar os padrões de raciocínio dos grupos de interesse, para então, elaborar e conduzir atividades eficientes e eficazes que promovam o desenvolvimento cognitivo dos indivíduos é um dos objetivos do grupo de pesquisa. Acredita-se que este trabalho contribui para mais um passo nessa direção.

This dissertation presents a series of neuroimaging investigations and achievements that strive to deepen and broaden our understanding of human problem solving and physics learning. Neuroscience conceives of dynamic relationships between behavior, experience, and brain structure and function, but how neural changes enable human learning across classroom instruction remains an open question. At the same time, physics is a challenging area of study in which introductory students regularly struggle to achieve success across university instruction. Research and initiatives in neuroeducation promise a new understanding into the interactions between biology and education, including the neural mechanisms of learning and development. These insights may be particularly useful in understanding how students learn, which is crucial for helping them succeed. Towards this end, we utilize methods in functional magnetic resonance imaging (fMRI), as informed by education theory, research, and practice, to investigate the neural mechanisms of problem solving and learning in students across semester-long University-level introductory physics learning environments. In the first study, we review and synthesize the neuroimaging problem solving literature and perform quantitative coordinate-based meta-analysis on 280 problem solving experiments to characterize the common and dissociable brain networks that underlie human problem solving across different representational contexts. Then, we describe the Understanding the Neural Mechanisms of Physics Learning project, which was designed to study functional brain changes associated with learning and problem solving in undergraduate physics students before and after a semester of introductory physics instruction. We present the development, facilitation, and data acquisition for this longitudinal data collection project. We then perform a sequence of fMRI analyses of these data and characterize the first-time observations of brain networks underlying physics problem solving in students after university physics instruction. We measure sustained and sequential brain activity and functional connectivity during physics problem solving, test brain-behavior relationships between accuracy, difficulty, strategy, and conceptualization of physics ideas, and describe differences in student physics-related brain function linked with dissociations in conceptual approach. The implications of these results to inform effective instructional practices are discussed. Then, we consider how classroom learning impacts the development of student brain function by examining changes in physics problem solving-related brain activity in students before and after they completed a semester-long Modeling Instruction physics course. Our results provide the first neurobiological evidence that physics learning environments drive
the functional reorganization of large-scale brain networks in physics students. Through this collection of work, we demonstrate how neuroscience studies of learning can be grounded in educational theory and pedagogy, and provide deep insights into the neural mechanisms by which students learn physics.