Thesis (Ph. D.)--University of Massachusetts at Amherst, 1998. / eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2000. / Includes bibliographical references (leaves 90-95).

Thesis (Ph. D.)--University of Texas at El Paso, 2009. / Title from title screen. Vita. CD-ROM. Includes bibliographical references. Also available online.

Thesis (M.S.)--State University of New York at Binghamton, Department of Psychology, 2008. / Includes bibliographical references.

The present study attempted to merge classes of Islamic and Christian stimuli. Using a match-to-sample procedure, participants were trained on a series of conditional discriminations that resulted in the formation of one equivalence class containing Islamic stimuli and one class containing Christian stimuli. These classes shared two common members. After demonstrating equivalence, participants were tested for a merger of the two classes. If the classes fail to merge, participants viewed a video outlining the parallels between Islam and Christianity and were subsequently tested for a merger of classes again. For participants still failing to merge the classes, direct training was provided before a final test for a merger of classes was conducted. Accuracy and response latencies on tests for a merger of classes were examined to determine if a nodal distance effect is observed. Six participants merged the classes following conditional discrimination training. An additional five participants were exposed to video training and direct training after which four participants still failed to merge the classes.

abstract: Categories are often defined by rules regarding their features. These rules may be intensely complex yet, despite the complexity of these rules, we are often able to learn them with sufficient practice. A possible explanation for how we arrive at consistent category judgments despite these difficulties would be that we may define these complex categories such as chairs, tables, or stairs by understanding the simpler rules defined by potential interactions with these objects. This concept, called grounding, allows for the learning and transfer of complex categorization rules if said rules are capable of being expressed in a more simple fashion by virtue of meaningful physical interactions. The present experiment tested this hypothesis by having participants engage in either a Rule Based (RB) or Information Integration (II) categorization task with instructions to engage with the stimuli in either a non-interactive or interactive fashion. If participants were capable of grounding the categories, which were defined in the II task with a complex visual rule, to a simpler interactive rule, then participants with interactive instructions should outperform participants with non-interactive instructions. Results indicated that physical interaction with stimuli had a marginally beneficial effect on category learning, but this effect seemed most prevalent in participants were engaged in an II task. / Dissertation/Thesis / Doctoral Dissertation Psychology 2014

Master of Science / Department of Computer Science / William H. Hsu / This work describes a comparative study of empirical methods for categorization of new articles within text corpora: unsupervised learning for an unlabeled corpus of text documents and supervised learning for hand-labeled corpus. The goal of text categorization is to organize natural language (i.e. human language) documents into categories that are either predefined or that are inherently grouped by similar meaning. The first approach, automatic classification of texts, can be handy when handling massive amounts of data and has many applications such as automated indexing of scientific articles, spam filtering, classification of news articles etc. Classification using supervised or semi-supervised inductive learning involves labeled data, which can be expensive to acquire and may require semantically deep understanding of the meaning of texts. The second approach falls under the general rubric of document clustering, based on the statistical distribution and co-occurrence of words in a full-text document. Developing a full pipeline for document categorization draws on methods from information retrieval (IR), natural language processing (NLP), and machine learning (ML). In this project, experiments are conducted on two text corpora: news aggregator data, which contains news headlines collected from a web aggregator and a news data set consisting of original news articles from the British Broadcasting Corporation (BBC). First, the training data is developed from these corpora. Next, common types of supervised classifiers, such as linear, Bayesian, ensemble models and support vector machines (SVM) are trained, on the labelled data and the trained classification models are used to predict the category of an article, given the related text. The results obtained are analyzed and compared to determine the best performing model. Then, two unsupervised learning techniques – k-means and Latent Dirichlet Allocation (LDA) are applied to obtain clusters of data points. k-means separates the documents into disjoint clusters of similar news. Additionally, LDA was used, which treats documents as a mixture of topics, to find latent topics in text. Finally, visualizations of the results are produced for evaluation: to allow qualitative assessment of cluster separation in the case of unsupervised learning, or to understand the confusion matrix for the supervised classification task by heat map visualization as well as precision, recall, and other holistic metrics. From an application standpoint, the unsupervised techniques applied can be used to find news that are similar in content and can be categorized under a specific topic.

In 2010 WikiLeaks released a number of secret and classified documents that contained information pertaining to the United States government. Since then, WikiLeaks and the United States government have been engaged in a rhetorical battle over the circulation of information. Using membership categorization analysis (MCA) as an analytical technique this thesis answers the following research question: what form(s) of politics are made possible as the result of the social orders produced by both WikiLeaks’ and the United States government’s public discourse on the circulation of information? After analyzing a sample of the related discourse, it became clear that the disagreement between WikiLeaks and the United States government is much greater than different views on the distribution of, and access to, information. Rather, the major issue is that the discourses produced by representatives of both organizations constitute two similar and yet somehow opposing social orders. The social orders produced result in different forms of politics and democracy. In turn, this involves each side thinking differently about transparency, the public, the government, the law, and the media.

To acquire language, children must learn how to categorize objects on the basis of the meanings that cultures have assigned to the objects. A series of six experiments tested how preschool-aged children categorize familiar objects. Each experiment used a matching-to-sample format in which children matched pictures of familiar objects (comparisons) to a sample stimulus picture. The sample and one comparison related taxonomically (on the basis of similar features) and the other comparison related thematically (on the basis of function) from which the children were to find another stimulus that was the same as the sample. Each experiment was a systematic replication of published research and of the prior experiment. In all six experiments, these preschool-aged children demonstrated a statistically significant preference for the taxonomic stimulus. No statistically significant differences were found between genders. The results of these six experiments did not support the development trend described in the majority of the extant literature. These findings are also contrary to the research literature, with one noted exception.

Research into the nature and content of visual long-term memory has investigated what aspects of its representation may account for the remarkable ability we have to remember large amounts of detailed visual information. One theory proposed is that visual memories are supported by an underlying structure of conceptual knowledge around which visual information is organized. However, findings in memory for visual information learned in a visual search task were not explained by this theory of conceptual support, and a new theory is proposed that incorporates the importance of episodic, task-relevant visual information into the organizational structure of visual memory. The current study examined visual long-term memory organization as evidenced by retroactive interference effects in memory for objects learned in a visual search. Four experiments were conducted to examine the amount of retroactive interference induced based on aspects in which interfering objects were related to learned objects. Specifically, episodically task-relevant information about objects was manipulated between conditions based on search instructions. Aspects of conceptual category, perceptual information (color), and context (object role in search) were examined for their contribution to retroactive interference for learned objects. Findings indicated that when made episodically task-relevant, perceptual, as well as conceptual, information contributed to the organization of visual long-term memory. However, when made episodically non-relevant, perceptual information did not contribute to memory organization, and memory defaulted to conceptual category organization. This finding supports the theory of an episodically defined organizational structure in visual long-term memory that is overlaid upon an underlying conceptual structure.