<p> The web has become more than a supplementary information resource but a valuable and pervasive tool for nearly all aspects of daily life including social and community participation, health promotion, creative pursuits, education, and employment opportunity. However, the web is not yet easily accessible to all people, particularly those with cognitive disabilities who encounter many challenges with access and use of the web including limited accessibility of online content and difficulty with content comprehension. Furthermore, little is documented about how individuals with cognitive disabilities who currently use the web are overcoming or being inhibited by these challenges. Much of what is documented is anecdotal or generalized as broad technical guidance rather than providing methods to empower individual end users. This research explores which websites people with cognitive disabilities use and do not use and what challenges and successes they encounter with those websites. We developed the SimpleWebAnywhere tool to address the above research needs and serve as a technology probe to determine how content simplification affects web use by people with cognitive disabilities. We explored personalizable content transformation techniques, including advertisement removal, content extraction, and text to speech, to make webpages easier to use and comprehend. We found that many people with cognitive disabilities frequently access the web for long periods of time despite popular opinion to the contrary. Web access is preferred via mobile platforms, such as smartphones and tablet computers. Users had a strong preference for entertainment content largely comprised of images, videos, and games but did not necessarily have difficulty using or understanding long, complex textual content. An intercommunity approach of combining existing open source software to provide personalized content manipulations was found to be an effective method to improve web accessibility for people with cognitive disabilities.</p>

<p> News organizations, non-profits, and even government agencies use information graphics to advertise and communicate their messages. Data visualizations are used heavily in these graphics, but they also often incorporate unusual design elements to help catch viewers&rsquo; eyes. In the struggle to rise to the top of the crowd, the data visualizations in infographics are often embellished with additions and modifications to the raw chart. The general consensus is that these embellishments can make charts less effective at communicating information, but most of them have never been tested to see if this is true. This work examines the factors in bar, pie, and donut charts that affect our perception of the charts.</p><p> I approach this in two different ways, both using a series of surveys on Mechanical Turk. The work on pie charts examines the individual contribution of arc-length, angle, and area variables so that embellishments may be evaluated based on their use of visual variables. The bar chart work examines some of the most common embellishments designers make to bar charts. This approach allows the isolated study of embellishments to determine which hinder or contribute the most to our perception of charts. I conclude with concrete recommendations based on the findings of the studies. My results show that conventional wisdom about how these charts are perceived is not always correct, and some types of embellishments are harmful while others have virtually no effect.</p>

Thesis (Ph.D.)--Indiana University, Dept. of Computer Science, 2009. / Title from PDF t.p. (viewed on Feb. 10, 2010). Source: Dissertation Abstracts International, Volume: 70-05, Section: B, page: 3009. Adviser: David B. Leake.

Thesis (Ph.D.)--Indiana University, Computer Science and Cognitive Science, 2007. / Title from PDF t.p. (viewed Nov. 11, 2008). Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6350. Advisers: Robert L. Goldstone; Michael Gasser.

<p> Research on reward-driven learning has produced and substantiated theories of model-free and model-based reinforcement learning (RL), which respectively explain how humans and animals learn reflexive habits and build prospective plans. A highly developed line of work has unearthed the role of striatal dopamine in model-free learning, while the prefrontal cortex (PFC) appears to critically subserve model-based learning. The recent theory of meta-reinforcement learning (meta-RL) explained a wide array of findings by positing that the model-free dopaminergic reward prediction error trains the recurrent prefrontal network to execute arbitrary RL algorithms&mdash;including model-based RL&mdash;in its activations. </p><p> In parallel, a nascent understanding of a third reinforcement learning system is emerging: a non-parametric system that stores memory traces of individual experiences rather than aggregate statistics. Research on such <i>episodic learning</i> has revealed its unmistakeable traces in human behavior, developed theory to articulate algorithms underlying that behavior, and pursued the contention that the hippocampus is centrally involved. These developments lead to a set of open questions about (1) how the neural mechanisms of episodic learning relate to those underlying incremental model-free and model-based learning and (2) how the brain arbitrates among the contributions of this abundance of valuation strategies. </p><p> This thesis extends meta-RL to provide an account for episodic learning, incremental learning, and the coordination between them. In this theory of episodic meta-RL (EMRL), episodic memory reinstates activations in the prefrontal network based on contextual similarity, after passing them through a learned gating mechanism (Chapters 1 and 2). In simulation, EMRL can solve episodic contextual water maze navigation problems and episodic contextual bandit problems, including those with Omniglot class contexts and others with compositional structure (Chapter 3). Further, EMRL reproduces episodic model-based RL and its coordination with incremental model-based RL on the episodic two-step task (Vikbladh et al., 2017; Chapter 4). Chapter 5 discusses more biologically detailed extensions to EMRL, and Chapter 6 analyzes EMRL with respect to a set of recent empirical findings. Chapter 7 discusses EMRL in the context of various topics in neuroscience.</p><p>

<p> The use of language is one of the defining features of human cognition. Focusing here on two key features of language, <i>productivity</i> and <i>robustness</i>, I examine how basic questions regarding linguistic representation can be approached with the help of probabilistic generative language models, or PGLMs. These statistical models, which capture aspects of linguistic structure in terms of distributions over events, can serve as both the product of language learning and as prior knowledge in real-time language processing. In the first two chapters, I show how PGLMs can be used to make inferences about the nature of people's linguistic representations. In Chapter 1, I look at the representations of language learners, tracing the earliest evidence for a noun category in large developmental corpora. In Chapter 2, I evaluate broad-coverage language models reflecting contrasting assumptions about the information sources and abstractions used for in-context spoken word recognition in their ability to capture people's behavior in a large online game of &ldquo;Telephone.&rdquo; In Chapter 3, I show how these models can be used to examine the properties of lexicons. I use a measure derived from a probabilistic generative model of word structure to provide a novel interpretation of a longstanding linguistic universal, motivating it in terms of cognitive pressures that arise from communication. I conclude by considering the prospects for a unified, expectations-oriented account of language processing and first language learning.</p><p>

<p> <b>Background:</b> Software engineering teams must have a shared understanding of the system design in order to work independently but successfully integrate their code. These issues of understanding are important to project success but difficult to investigate with current approaches. Current techniques for investigating shared understanding, such as interviews or questionnaires, are limited by the difficulty of team members to externalize knowledge relevant to shared understanding. </p><p> <b>Aims:</b> This research has two goals. The first goal is to identify and validate a measure of shared understanding that researchers can use to investigate issues of shared understanding in software design. The second goal is to evaluate the potential for this measure to be used by practitioners to improve the software design process. </p><p> <b>Method:</b> A measure of shared understanding was developed by adapting an approach from the Team Mental Models literature. Five student teams and two industrial teams were recruited to evaluate the measure empirically. The validity of the measure, the significance of the differences in understanding found, and the applicability for design process improvement were investigated using qualitative techniques, including group interviews, observation, and questionnaires. </p><p> <b>Results:</b> When ranked by the measure of shared understanding, high ranking design concepts were generally, but not consistently, found to be associated with greater similarity of understanding than low ranking concepts. This supports a finding that the measure is valid, but imprecise. Although no specific misunderstandings were identified within the team, some team members found the discussion, guided by the measured differences, valuable for improving shared understanding generally. </p><p> <b>Conclusions:</b> The results support the use of the measure as a tool to investigate shared understanding so long as consideration is given to its limitations. It is premature for practitioners to use the measure to improve the design process. The results are based on only two industrial teams without a history of failures related to shared understanding. Future research should re-evaluate the measurement in different contexts. Guidance is given for additional research to refine the measurement.</p>

<p> Survey theory developed as a means to overcome problems with design and analysis is inherent in early research. Survey sampling methodology improves the quality of information collected, ensures the accuracy of data analysis, and reduces the cost of research. Technology drives the evolution of data collection and analysis that is required in survey sampling. In turn, this influences survey sampling techniques. I investigate the history of survey sampling, current survey sampling theory, and current theory applied to two examples: 1) a stratified market research survey, and 2) a psychological survey for health science research. </p><p> The market research survey was an original design using a specific methodology: conduct pre-interviews on a small sample, develop survey questions based on the qualitative research, stratify the target sample during data collection, and perform data analysis on the resulting cross-sectional data. The second survey utilizes well-developed and tested health measurement instruments that have already been developed and tested. The resulting longitudinal data are then scored and analyzed.</p>

Thesis (Ph.D.)--Indiana University, Dept. of Computer Science and the Dept. of Cognitive Science, 2008. / Title from PDF t.p. (viewed on May 13, 2009). Source: Dissertation Abstracts International, Volume: 69-08, Section: B, page: 5071. Advisers: Mike Gasser; Lisa Gershkoff-Stowe.

Thesis (Ph.D.)--Indiana University, Dept. of Computer Science and Cognitive Science, 2009. / Title from PDF t.p. (viewed on Oct. 8, 2009). Source: Dissertation Abstracts International, Volume: 70-02, Section: B, page: 1368. Advisers: Michael Gasser; Linda B. Smith.