Increasing amounts of data are being produced and consumed on a daily basis. Every mouse movement and click on a website can be analyzed to discover usage patterns and cognitive load (Jenkins et al., 2014), companies mine purchase histories to discover customer shopping patterns (Brin et al., 1997) and historical business transaction information can be used to improve business processes (Ghattas et al., 2014). Using sophisticated algorithms, data can be turned into information that helps guide marketers, policy makers, business managers, and other decision-makers. However, history has shown that increases in the amount and quality of information do not necessarily lead to better decision outcomes (Dawes et al., 1989). Human decision-makers may fail to understand the information, ignore it, or simply not believe it. Methods for effectively conveying information to humans must be studied so that the full value of information systems can be realized. This dissertation uses three studies to explain the factors that make technology persuasive. In the first study, attitudes toward technology measure how beliefs about technology influence the way people process information. Ordering effects are also examined to determine how people view information from decision support systems, and to find the optimal time to present information to decision-makers. In the second study, the persuasive power of text and audio modalities are compared. Additionally, the loss aversion bias is investigated to determine the utility of leveraging this cognitive bias in a technology context. In the third study, Protection Motivation Theory (Rogers, 1983) is used to extend the loss aversion model from study two. The study also investigates how message vividness and user participation through software personalization influence attitudes and behavior. Together, these experiments extend existing theoretical frameworks while giving actionable guidance to information systems practitioners. The studies demonstrate the importance of understanding cognitive biases, attitudes toward technology, and message delivery in a decision support scenario. These investigations are the first step in creating a more comprehensive model of factors that influence the persuasive power of technology.

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Pretrial publicity is a problem that can affect the fair outcome of a trial, a right that is guaranteed by the U.S. Constitution. Research has indicated that potential jurors who are exposed to negative pretrial publicity are more likely to render 'guilty' verdicts (Ruva and McEvoy, 2008). The current study will entail an analysis of pretrial publicity and a case study of attitudes and beliefs surrounding the Casey Anthony trial. Participants included 309 undergraduates at the University of Central Florida. Results indicated that the majority of participants already hold negative biases and non-deliberate exposure influenced negative attitudes and beliefs.

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Yes / Many studies have examined the extent to which individuals’ probability judgments depart from Bayes’ theorem when revising probability estimates in the light of new information. Generally, these studies have not considered the implications of such departures for decisions involving risk. We identify when such departures will occur in two common types of decisions. We then report on two experiments where people were asked to revise their own prior probabilities of a forthcoming economic recession in the light of new information. When the reliability of the new information was independent of the state of nature, people tended to overreact to it if their prior probability was low and underreact if it was high. When it was not independent, they tended to display conservatism. We identify the circumstances where discrepancies in decisions arising from a failure to use Bayes’ theorem were most likely to occur in the decision context we examined. We found that these discrepancies were relatively rare and, typically, were not serious.

This descriptive qualitative study examines six eighth-grade students' thinking while solving probability problems. This study aimed to gather direct information on students' problem solving processes informed by the heuristics and biases framework. This study used purposive sampling (Patton, 1990) to identify eighth-grade students who were knowledgeable about probability and had reached the formal operational stage of cognitive development. These criterion were necessary to reduce the likelihood of students' merely guessing answers and important so that the researcher could distinguish between reasoning and intuition. The theoretical framework for this study was informed by Kahneman and Fredrick's (2002) recent revision to the heuristics and biases framework grounded in the research of Amos Tversky and Daniel Kahneman. Kahneman and Fredrick (2002) drew on dual process theory to explain systematic and predictable heuristic ways of thinking. Dual process theory hypothesizes that human thinking is divided into two different modes of processing. One mode, called System 1, is fast and linked to intuition, and the other, called System 2, is slow and linked to reasoning (Evans, 2008; Stanovich and West, 2000). Within dual process theory, System 1 thinking provides a credible system for explaining why people use heuristic thinking (Kahneman and Frederick, 2002). The recent revision to the heuristics and biases framework is focused on three heuristics, representativeness, conjunction fallacy, and availability. These three heuristics are believed to share the same mental process identified by Kahneman and Fredrick (2002), as the attribute substitution process. The clinical task based interview method was used in this study. This technique allowed the researcher to better observe and interact with the participants while exploring the students' probability thinking. The researcher also used think-aloud protocols to better reveal the organic thinking patterns of the students in real time (Ericsson and Simon, 1980; Fox, Ericsson, and Bets, 2010; Van Someren, Barnard, and Sandberg, 1994). The data from the interviews were analyzed using the constant comparison method (Glaser, 1965). This analysis revealed three categories that were combined with other analyses to create profiles for various thinking patterns observed by the researcher. The researcher identified patterns of thinking by students that were consistent with System 1 thinking and associated with the attribute substitution process (Kahneman and Fredrick, 2002). There were also situations in which students demonstrated ways of thinking consistent with System 2 thinking. However, unexpected ways of thinking were also identified by the researcher. For example, there were occasions when students substituted their fraction knowledge when solving probability problems and even seemed to equate probability with fractions. This type of thinking was referred to as the content substitution process in this study. This process occurred when students were using System 1 thinking as well as other types of thinking. In addition, the researcher observed students with thinking patterns that contained characteristics of both System 1 and System 2, which is referred to as slow intuition in this study. Slow intuition seemed to affect students' problem solving strategies as they wavered between multiple problem solving strategies that included either of the two substitution processes: attribute substitution and content substitution. This study contributes to the body of knowledge related to probabilistic thinking. In particular, this study informs our understanding of heuristic thinking used by eighth-grade students when solving probability problems. Further, teaching practices that draw on Fischbein's (1975, 1987) general notion of intuition might be developed and used to improve probability reasoning skills. These teaching practices target students that depend on the attribute substitution process and/or the content substitution process. Each of these heuristic ways of thinking may require different instructional techniques to help students develop more sound ways of thinking about probability. Regardless, teachers need to be informed of the extent that some students rely on their fraction knowledge when solving probability problems. / Ph. D.

This thesis investigates the role of phonetic biases and systemic effects in the actuation of sound change through computer simulations and experimental methods. Phonetic biases are physiological and psychoacoustic constraints on speech. One example is vowel undershoot: vowels sometimes fail to reach their phonetic targets due to limitations on the speed of the articulators. Phonetic biases are often paralleled by phonological patterns. For instance, many languages exhibit vowel reduction, a phonologised version of undershoot. To account for these parallels, a number of researchers have proposed that phonetic biases are the causal drive behind sound change. Although this proposal seems to solve the problem of actuation, its success is only apparent: while it might be able to explain situations where sound change occurs, it cannot easily explain the lack of sound change, that is, stasis. Since stability in sound systems seems to be the rule rather than the exception, the bias-based approach cannot provide an adequate account of their diachronic development on its own. The problem of bias-based accounts stems from their focus on changes affecting individual sound categories, and their neglect of system-wide interactions. The factors that affect speech production and perception define an adaptive landscape. The development of sound systems follows the topology of this landscape. When only a single category is investigated, it is easy to take an overly simplistic view of this landscape, and assume that phonetic biases are the only relevant factor. It is natural that the predicted outcomes will be simple and deterministic if such an approach is adopted. However, when we look at an entire sound system, other pressures such as contrast maintenance also become relevant, and the range of possible outcomes is much more diverse. Phonetic biases can still skew the adaptive landscape towards themselves, making phonetically natural outcomes more likely. However, their effects will often be countered by other pressures, which means that they will not be satisfied in every case. Sound systems move towards peaks in the adaptive landscape, or local optima, where the different pressures balance each other out. As a result, the system-based approach predicts stability. This stability can be broken by changes in the pressures that define the adaptive landscape. For instance, an increase or a decrease in functional load or a change in lexical distributions can create a situation where the sound system is knocked out of an equilibrium and starts evolving towards a new stable state. In essence, the adaptive landscape can create a moving target for the sound system. This ensures that both stability and change are observed. Therefore, this account makes realistic predictions with respect to the actuation problem. This argument is developed through a series of computer simulations that follow changes in artificial sound systems. All of these simulations are based on four theoretical assumptions: (i) speech production and perception are based on probabilistic category representations; (ii) these category representations are subject to continuous update throughout the lifetime of an individual; (iii) speech production and perception are affected by low-level universal phonetic biases; and (iv) category update is inhibited in cases where too many ambiguous tokens are produced due to category overlap. Special care is taken to anchor each of these assumptions in empirical results from a variety of fields including phonetics, sociolinguistics and psycholinguistics. Moreover, in order to show that the results described above follow directly from these theoretical assumptions and not other aspects of these models, the thesis demonstrates that exemplar and prototype models produce the same dynamics with respect to the observations above, and that the number of speakers in the model also does not have a significant influence on the outcomes. Much of the thesis focuses on rather abstract properties of simulated systems, which are difficult to test in a systematic way. The last chapter complements this by presenting a concrete example, which shows how the simulations can be linked to empirical data. Specifically, I look at the effect of lexical factors on the strength of contextual effects in sound categories, using the example of the voicing effect, whereby vowels are longer before voiced obstruents than they are before voiceless ones. The simulations implemented in this chapter predict a larger effect in cases where a given vowel category occurs equally frequently in voiced and voiceless environments, and a smaller difference where one of the environments dominates the lexical distribution of the vowel. This prediction is borne out in a small cross-linguistic production experiment looking at voicingconditioned vowel length patterns in French, Hungarian and English. Although this is only one of many predictions that fall out of the theory of sound change developed in this thesis, the success of this experiment is a strong indication that the research questions it brings into focus are worth investigating.

Data shows that many inventors continue to expend resources on their inventions even after they have received expert advice suggesting that they cease effort. Using a sample of inventors seeking outside advice from a Canadian evaluative agency, this paper examines how overconfidence, optimism, and illusion of control explain this fact. While overconfidence did not have a significant effect on inventor's decisions, illusion of control and optimism did have an effect. An additional interesting finding is that the more time people have spent working on inventions, the more likely they are to discount this expert advice.

<p>Individuals have the tendency to believe that they have complete information when making decisions. In many contexts this propensity allows for swift, efficient, and generally effective decision making. However, individuals cannot always see a representative picture of the world in which they operate. This paper examines judgment in censored environments where a constraint, the censorship point, systematically distorts the sample observed by a decision maker. Random instances beyond the censorship point are observed at the censorship point, while instances below the censorship point are observed at their true value. Many important managerial decisions occur in censored environments, such as inventory, risk-taking, and employee evaluation decisions. This empirical work demonstrates a censorship bias - individuals tend to rely too heavily on the observed censored sample, biasing their beliefs about the underlying population. Further, the censorship bias is exacerbated for higher rates of censorship, higher variance in the population, and higher variability in the censorship points. Evidence from four studies demonstrates how the censorship bias can cause managers to underestimate demand for their goods, over-estimate risk in their environments, and underappreciate the capabilities of their employees, which can lead to undesirable outcomes for organizations.</p> / Dissertation

Pollinator-driven selection is thought to drive much of the extraordinary diversity of flowering plants. Plants that produce floral traits preferred by particular pollinators are more likely to receive conspecific pollen and to evolve further adaptations to those pollinators that enhance pollination and ultimately generate floral diversity. Two mechanisms in particular, sensory bias and learning, are thought to explain how pollinator preference can contribute to divergence and speciation in flowering plants. While the preferences of pollinators, such as bees, flies, and birds, are frequently implicated in patterns of floral trait evolution, the role of learning in generating reproductive isolation and trait divergence for different floral types within plant populations is not well understood. Floral color polymorphism in particular provides an excellent opportunity to examine how pollinator behavior and learning might maintain the different floral morphs. In this study we asked if bumble bees showed innate preferences for different color morphs of the pollen-only plant Solanum tridynamum, whether bees formed preferences for the morphs with which they had experience collecting pollen from, and the strength of those learned preferences. Using an absolute conditioning protocol, we gave bees experience collecting pollen from a color polymorphic plant species that offered only pollen rewards. Despite initially-naïve bees showing no apparent innate bias toward human-white versus human-purple flower morphs, we did find evidence of a bias in learning. Specifically, bees learned strong preferences for purple corollas, but learned only weak preferences for hypochromic (human-white) corollas. We discuss how our results might explain patterns of floral display evolution, particularly as they relate to color polymorphisms. Additionally, we propose that the ease with which floral visual traits are learned—i.e., biases in learning—can influence the evolution of floral color as a signal to pollinators.