Approved for public release, distribution is unlimited / Extreme examples like the Spanish Flu pandemic of 1918 make clear the devastating impact that communicable diseases can have on military readiness. It is highly desirable to have models and tools that can be used to evaluate the course of a disease over time. These tools can help assess the effectiveness of strategies employed to contain the outbreak such as constraining movement, wearing protective gloves or masks, closing high traffic areas, etc. Armed with these tools, a medical practitioner can better assess the right course of action in a time critical situation. The primary difficulty with creating models and simulations for this purpose is that disease spread depends upon the details of human behavior and environmental variables which are not accounted for in current mathematical models. The likelihood that a particular individual will catch a given disease depends upon such specifics as where he works, whom he interacts with, where he sleeps, what he eats, his habits of personal hygiene, etc. It is hypothesized that a software disease simulation can combine agents that mimic human behavior, a ship specific environment, and disease specific attributes to more accurately model the spread of disease aboard ship than a mathematical model. / Outstanding Thesis / Lieutenant, United States Navy

Designing user-interfaces so that first-time or infrequent users can accomplish their goals by exploration has been an enduring challenge in Human-Computer Interaction. Iterative user-testing is an effective but costly method to develop user-interfaces that support use through exploration. A complementary method is to use modeling tools that can generate predictions of user exploration given a user-interface and a goal description. Recent computational models of goal-directed user exploration have focused on predicting user exploration of websites and demonstrated how predictions can inform user-interface design. These models employ the common concepts of label following and information scent: that the user's choice is partly determined by the semantic relevance between the user's goal and the options presented in the user-interface. However, in addition to information scent, other factors including the layout position and grouping of options in the user-interface also affect user exploration and the likelihood of success. This dissertation contributes a new model of goal-directed user exploration, called CogTool- Explorer, which considers the layout position and the grouping of options in the user-interface in concert with a serial evaluation visual search process and information scent. Tests show that predictions from CogTool-Explorer match participant data better than alternative models that do not consider layout position and grouping. This dissertation work has also integrated the CogTool- Explorer model into an existing modeling tool, called CogTool, making it easier for other researchers and practitioners to setup and generate predictions of likely user exploration paths and task performance using CogTool-Explorer.

Typescript (photocopy). / Digitized by Kansas State University Libraries

There are more neurons in the human brain than seconds in a lifetime. Given this incredible number how can we hope to understand the computations carried out by the full ensemble of neural firing patterns? And neural activity is not the only substrate available for computations. The incredible diversity of function found within biological organisms is matched by an equally rich reservoir available for computation. If we are interested in the metamorphosis of a caterpillar to a butterfly we could explore how DNA expression changes the cell. If we are interested in developing therapeutic drugs we could explore receptors and ion channels. And if we are interested in how humans and other animals interpret incoming streams of sensory information and process them to make moment-by-moment decisions then perhaps we can understand much of this behavior by studying the firing rates of neurons. This is the level and approach we will take in this thesis. Given this diversity of potential reservoirs for computation, combined with limitations in recording technologies, it can be difficult to satisfactorily conclude that we are studying the full set of neural dynamics involved in a particular task. To overcome this limitation, we augment the study of neural activity with the study of artificial recurrent neural networks (RNNs) trained to mimic the behavior of humans and other animals performing experimental tasks. The inputs to the RNN are time-varying signals representing experimental stimuli and we adjust the parameters of the RNN so its time-varying outputs are the desired behavioral responses. In these artificial RNNs we have complete information about the network connectivity and moment-by-moment firing patterns and know, by design, that these are the only computational mechanisms being used to solve the tasks. If the artificial RNN and electrode recordings of real neurons have the same dynamics we can be more confident that we are studying the sufficient set of biological dynamics involved in the task. This is important if we want to make claims about the types of dynamics required, and observed, for various computational tasks, as is the case in Chapter 2 of this thesis. In Chapter 2 we develop tests to identify several classes of neural dynamics. The specific neural dynamic regimes we focus on are interesting because they each have different computational capabilities, including, the ability to keep track of time, or preserve information robustly against the flow of time (working memory). We then apply these tests to electrode recordings from nonhuman primates and artificial RNNs to understand how neural networks are able to simultaneously keep track of time and remember previous experiences in working memory. To accomplish both computational goals the brain is thought to use distinct neural dynamics; stable neural trajectories can be used as a clock to coordinate cognitive activity whereas attractor dynamics provide a stable mechanism for memory storage but all timing information is lost. To identify these neural regimes we decode the passage of time from neural data. Additionally, to encode the passage of time, stabilized neural trajectories can be either high-dimensional as is the case for randomly connected recurrent networks (chaotic reservoir networks) or low-dimensional as is the case for artificial RNNs trained with backpropagation through time. To disambiguate these models we compute the cumulative dimensionality of the neural trajectory as it evolves over time. Recurrent neural networks can also be used to generate hypotheses about neural computation. In Chapter 3 we use RNNs to generate hypotheses about the diverse set of neural response properties seen during spatial navigation, in particular, grid cells, and other spatial correlates, including border cells and band-like cells. The approach we take is 1) pick a task that requires navigation (spatial or mental), 2) create a RNN to solve the task, and 3) adjust the task or constraints on the neural network such that grid cells and other spatial response patterns emerge naturally as the network learns to perform the task. We trained RNNs to perform navigation tasks in 2D arenas based on velocity inputs. We find that grid-like spatial response patterns emerge in trained networks, along with units that exhibit other spatial correlates, including border cells and band-like cells. Surprisingly, the order of the emergence of grid-like and border cells during network training is also consistent with observations from developmental studies. Together, our results suggest that grid cells, border cells and other spatial correlates observed in the Entorhinal Cortex of the mammalian brain may be a natural solution for representing space efficiently given the predominant recurrent connections in the neural circuits. All the tasks we have considered so far in this thesis require memory, but in Chapter 4 we explicitly explore the interactions between multiple memories in a recurrent neural network. Memory is the hallmark of recurrent neural networks, in contrast to standard feedforward neural networks where all signals travel in one direction from inputs to outputs and the network contains no memory of previous experiences. A recurrent neural network, as the name suggests, contains feedback loops giving the network the computational power of memory. In this chapter we train a RNN to perform a human psychophysics experiment and find that in order to reproduce human behavior, noise must be added to the network, causing the RNN to use more stable discrete memories to constrain less stable continuous memories.

Household air pollution (HAP) is responsible for almost 4 million premature deaths every year, a burden that is primarily carried by women and children in developing countries. The mortality and morbidity impact of HAP can be significantly alleviated through clean cookstove interventions. However, for these interventions to be effective, the new intervention stove must be a substantially cleaner technology and adoption should be high and sustained over time. Woody biomass is the fuel of choice in many developing communities, and contributes substantially to HAP. Several organizations have launched clean cooking interventions to address this issue. However, the majority of those interventions do not address adoption related challenges, that they often face. This thesis explores previous studies on Human-Centered Design (HCD) and the impact of feedback and data access on behavior change. It details a HCD process and methodology applied during the design process of an air quality feedback system, to improve adoption of liquefied petroleum gas (LPG) cookstoves in Rwanda. The feedback system is intended to provide real-time air quality information to stove users and potentially encourage them to abandon traditional biomass cookstoves in favor of the cleaner LPG stoves.

Burnout is a serious work related syndrome that is a result of exposure to chronic work stress. In addition to the consequences of burnout on the individual, the symptoms of burnout can adversely affect the organization, the clients the individual works with and the individual's close family and friends. The literature has focused on the history of burnout and the level of burnout experienced by various high stress occupations; however there has not been extensive research into the role personality traits play in burnout. The main research question of this study was to identify personality traits that are more susceptible to burnout among correctional workers. This research utilized the survey research method by having participants voluntarily complete a demographics form, the Maslach Burnout Inventory for Human Service Workers, and the Big Five Inventory. Data was collected through an online questionnaire (N=169). Data was analyzed by correlation analysis and two step multiple regression using demographics and the individual components of burnout. The results suggested that individuals possessing the personality trait Neuroticism experienced high levels of Emotional Exhaustion and Depersonalization. The results also suggested that the length of years employed had no relationship to burnout. The study found that years worked, type of work and marital status on their own did not have any relationship with burnout; however when coupled with personality traits. The findings also showed that Neuroticism was the only personality trait that was associated with all three dimensions of burnout. These findings can assist organizations with identifying individuals in the field of corrections who may be predisposed to burnout and allow for early intervention. As a result, the interventions can lead to social change where individuals can be healthier, happier, more fulfilled and better able to protect and service the clients, the organization and the public.

This thesis is a defence of non-introspective simulationism. It seeks to explain how we acquire everyday behavioural and psychological beliefs (henceforth interpretational beliefs) regarding both ourselves and others. The thesis is in three parts; the first states non-introspective simulationism, the second surveys some relevant empirical findings and shows how simulationism explains (or at least accomodates) these, and the third compares simulationism with rival theories. The two main claims of non-introspecitve simulationism (as defended) are: simulation is central to the acquisition of interpretational beliefs. Introspection has no role whatever. Further central claims are: beliefs about our own currently intended behaviours are acquired by practical reasoning. Other interpretational beliefs are, in various ways, the product of simulation. Simulation requires little if any machinery not already required for practical reasoning. Knowledge of our own psychological states is acquired after and as a result of knowledge of others�. Knowledge of phenomenal states is unnecessary for mastery of folk psychology and the product of dinkum science.

University of Macau / Faculty of Social Sciences and Humanities / Department of Psychology

University of Macau / Faculty of Social Sciences and Humanities / Department of Psychology

Differential framing occurs when individuals with different latent motives assign qualitatively different meanings to the same attributes or events in the environment (James&Mazerolle, 2002; James&McIntyre, 1996). The implications of this phenomenon for the explanation and prediction of behavior are substantial: In perfectly logical fashions, individuals in exactly the same situation have qualitatively different experiences. In this way, differential framing mediates the relationship between motives and the behaviors that comprise traits. This dissertation tested several propositions associated with this phenomenon, and the results tentatively suggest that individuals with contrasting motives form qualitatively distinct impressions of the same organizational cultures.