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Akademisk avhandling--Estetik--Uppsala Universitet, 2001. / Bibliogr. p. 283-296.

Thesis (M.Ed.)--Kutztown State College, 1964. / Source: Masters Abstracts International, Volume: 45-06, page: 2753. Typescript. Includes bibliographical references (leaves [53]-54)

Beliefs that are not the result of science-based interpretation of evidence (e.g., belief in ghosts or belief that prayer is effective) are extremely common. Science enthusiasts have expressed interest in automatic detection of non-science-based claims. This thesis intends to provide some first steps toward a solution, specifically aimed at detecting Twitter users who are likely or unlikely to take a science-based perspective on all topics. As part of this thesis, a set a Twitter users was labeled as being either "pro-science" (i.e. as having the view that beliefs are rational if and only if they are in accord with science-based reasoning) or "non-pro-science" (i.e. as having the view that beliefs may be reasonable even if they are not in accord with science-based reasoning). Word frequency ratios relative to a neutral dataset, and a simple topic alignment technique, suggest considerable linguistic divergence between the pro-science and non-pro-science users. High accuracy logistic regression classification using linguistic features of users' recent tweets support that idea. Supervised classification experiments suggest that the pro-science and non-pro-science perspectives are not only detectable from linguistic features, but that they can be abstracted away from particular topics (i.e. that the pro-science and non-pro-science perspectives are not inherently topic-specific). Results from distantly supervised classification suggest that using easily acquired, weakly labeled data may be preferable to the much slower process of individually labeling data for some applications, despite the pronounced inferiority to the fully supervised approach in terms of accuracy. The best classifier obtained in this thesis has an accuracy of 93.9%. / text

The capacity to mentally transcend the present moment affords the opportunity to relive former pleasures or preview the perils of things to come. According to the theory of embodied cognition, these imaginary events are made possible by the reactivation of the modality-specific sensory-motor components that accompany interaction with the environment (Barsalou, 1999, 2008; Wilson, 2002). But what happens when our mental simulations deviate from veridical experience? Guided by the observation that imagining the self from an outside point-of-view is a regular occurrence in the mental world (Nigro & Neisser, 1983), this thesis explored the sensory-motor activities that accompany first- and third-person imagery. As first-person (cf. third-person) simulations are closer in character to actual experience, it was anticipated that events imagined from this point-of-view would be accompanied by more pronounced embodied effects than those imagined from a third-person perspective. Online (i.e., during imagery) measures demonstrated that neural (Chapter 2) and motor (Chapter 3) activities were greater when imagining experiences from a first-person (cf. third-person) perspective. Extending the exploration of embodiment to offline (i.e., after imagery) effects, it was evidenced that simulated temperature contaminated person perception judgments (Chapter 4) and imaginary ingestion exacerbated consumption of unhealthy foods (Chapter 5), but only when the relevant sensory information was simulated from a first-person point-of-view. A final study contextualized these findings, identifying the natural occurrence of each perspective as well as individual differences that influence the vantage point most commonly adopted during spontaneous mental imagery. Taken together, these results suggest that first-person simulations are more body-based than their third-person counterparts. Imbued with theoretical and practical implications, the current thesis identifies a potential mechanism by which first- and third-person simulations give rise to disparate cognitive and behavioural outcomes, and furnishes a more nuanced understanding of embodied cognition.

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Degree: Master of Arts Department: Psychology / This study explored children’s perceptions of death from a Piagetian perspective. While a few international studies exist which have explored this issue, it was deemed useful to evaluate the perceptions of South African children. It seems that many researchers have not taken into account the importance of broader contextual experience in children’s perceptions of death, and it was felt that the current high death rate in South Africa may impact on both or either the rate and level at which children start to engage with and understand the concept of death. The participants of the current study were between the ages of five and twelve years, and were drawn from a Gauteng Place of Safety and a preschool. Individual, structured interviews were conducted on this cross-sectional population. The interviews contained questions adapted from Koocher’s (1973) research in this area, and responses were analysed by means of a traditional content analysis. It was found that children had a basic understanding of death concepts by the age of approximately seven years old. It was also found that the youngest age group of children possess an awareness of death, but tend to view it as reversible and not final. An understanding of the finality of death emerges around the age of seven. In general, the participant’s responses showed a close relationship between Piaget’s stages of cognitive development and how the young child reasons about death.

The idea of voting multiple decision rules was introduced in to statistics by Breiman. He used bootstrap samples to build different decision rules, and then aggregated them by majority voting (bagging). In regression, bagging gives improved predictors by reducing the variance (random variation), while keeping the bias (systematic error) the same. Breiman introduced the idea of bias and variance for classification to explain how bagging works. However, Friedman showed that for the two-class situation, bias and variance influence the classification error in a very different way than they do in the regression case. In the first part of the dissertation, we build a theoretical framework for ensemble classifiers. Ensemble classifiers are currently the best off-the-shelf classifiers available, and they are the subject of much current research in classification. Our main theoretical results arc two theorems about voting iid (independently identically distributed) decision rules. The bias consistency theorem guarantees that voting will not change the Bias set, and the convergence theorem gives an explicit rate of convergence. The two theorems explain exactly how ensemble classifiers work. We also introduce the concept of weak consistency as opposed to the usual strong consistency. A boosting theorem is derived for a distribution-specific situation with iid voting. In the second part of this dissertation, we discuss a special ensemble classifier called PERT. PERT is a voted random tree classifier for which each random tree classifies every training example correctly. PERT is shown to work surprisingly well. We discuss its consistency properties. We then compare its behavior to the NN (nearest neighbor) method and boosted c4.5. Both of the latter methods also classify every training example correctly. We call these types of classifiers “oversensitive” methods. We show that one reason PERT works is because of its “squeezing effect.” In the third part of this dissertation, we design simulation studies to investigate why boosting methods work. The outlier effect of PERT is discussed and compared to boosted and bagged tree methods. We obtain a new criterion (Bayes deviance) that measures the efficiency of a classification method. We design simulation studies to compare the efficiency of several common classification methods, including NN, PERT, and boosted tree method.