No description available.

Many previous studies have examined the effects of goal attributes on subsequent behavior and performance, with consistently positive findings. However, there are few studies of goal processes, i.e., how reactions to goal origin and subsequent shifts in goal origin are exhibited in behavioral and subjective domains. The present research viewed reactions to goal origins (self-set or assigned) and to shifts in goal origin within a theory of personal control/psychological reactance. In addition, two individual difference constructs, locus of control and Type A Behavior Pattern, were measured to study their relationships with the dependent variables. A laboratory experiment was conducted to examine several hypotheses drawn from the psychological reactance literature concerning the joint effects of Initial Goal Origin and subsequent Shifted Goal Origin on subjective and behavioral responses. The subjective responses included task and performance satisfaction, goal acceptance, preferences for increasing employee self-control in the workplace, and preferred method of goal-setting. Behavioral measures included two different operational definitions of performance: quantity and goal attainment. The analyses revealed weak support for the hypotheses. The manipulation check composite revealed that the groups perceived the manipulations along the Initial Goal Origin dimension. There were statistically significant differences for goal acceptance, with the two shift groups displaying a crossover pattern between trial blocks. Simple interaction effect analyses conducted at each level of the Shifted Goal Origin factor revealed a statistically significant interaction between Initial Goal Origin and Trial Blocks for the Shift level only. Goal attainment scores revealed an apparently practical, yet statistically nonsignificant, effect for the three-way interaction of the manipulated factors over trial blocks. Moreover, the pattern of correlations between goals and performance reversed in the predicted direction over trial blocks for the two shift groups. However, there were no significant differences for the factors on the raw performance, satisfaction, or preference variables. The individual difference variables did not make a significant contribution to the prediction of the dependent variables after the effects of group membership were statistically controlled. The results are discussed in terms of the limitations of the method used, the weak support for the framework which guided the research, and implications for further research and implementation of goal-setting systems in organizations. / Ph. D.

Most phenomena in engineering fields involve physical variables that can potentially be predicted using simple or complex mathematical models. However, traffic engineers and researchers are faced with a complex challenge since they have to deal with the human element. For instance, it can be stated that the biggest challenge facing researchers in the area of car-following theory relates to accounting for the human-in-the-loop while modeling the longitudinal motion of the vehicles. In fact, a major drawback of existing car-following models is that the human-in-the-loop is not modeled explicitly. This is specifically important since the output from car-following models directly impacts several other factors and measures of effectiveness, such as vehicle emissions and fuel consumption levels. The main contribution of this research relates to modeling and incorporating, in an explicit and independent manner, the human-in-the-loop component in car-following theory in such a way that it can be either activated or deactivated depending on if a human driver is in control of the vehicle. That would ensure that a car-following model is able to reflect the different control and autonomy levels that a vehicle could be operated under. Besides that, this thesis offers a better understanding of how humans behave and differ from each other. In fact, through the implementation of explicit parameters representing the human-in-the-loop element, the heterogeneity of human behavior, in terms of driving patterns and styles, is captured. To achieve its contributions, the study starts by modifying the maximum acceleration vehicle-dynamics model by explicitly incorporating parameters that aim to model driver behavior in its expression making it suitable for the representation of typical acceleration behavior. The modified variant of the model is demonstrated to have a flexible shape that allows it to model different types of variations that drivers can generate, and to be superior to other similar models in that it predicts more accurate acceleration levels in all domains. The resulting model is then integrated in the Rakha-Pasumarthy-Adjerid car-following model, which uses a steady-state formulation along with acceleration and collision avoidance constraints to model the longitudinal motion of vehicles. The validation of the model using a naturalistic dataset found that the modified formulation successfully integrated the human behavior component in the model and that the new formulation decreases the modeling error. Thereafter, this dissertation proposes a new car-following model, which we term the Fadhloun-Rakha model. Even though structurally different, the developed model incorporates the key components of the Rakha-Pasumarthy-Adjerid model in that it uses the same steady state formulation, respects vehicle dynamics, and uses very similar collision-avoidance strategies to ensure safe following distances between vehicles. Besides offering a better fit to empirical data, the Fadhloun-Rakha model is inclusive of the following characteristics: (1) it models the driver throttle and brake pedal input; (2) it captures driver variability; (3) it allows for shorter than steady-state following distances when following faster leading vehicles; (4) it offers a much smoother acceleration profile; and (5) it explicitly captures driver perception and control inaccuracies and errors. Through a quantitative and qualitative evaluation using naturalistic data, the new model is demonstrated to outperform other state-of-the-practice car-following models. In fact, the model is proved to result in a significant decrease in the modeling error, and to generate trajectories that are highly consistent with the observed car-following behavior. The final part of this study investigates a case in which the driver is excluded and the vehicles are operating in a connected environment. This section aims to showcase a scenario in which the human-in-the-loop is deactivated through the development of a platooning strategy that governs the motion of connected cooperative multi-vehicle platoons. / Doctor of Philosophy / Even though the study of the longitudinal motion of vehicles spanned over several decades leading to the development of more precise and complex car-following models, an important aspect was constantly overlooked in those models. In fact, due to the complexity of modeling the human-in-the-loop, the vehicle and the driver were almost always assumed to represent a single entity. More specifically, ignoring driver behavior and integrating it to the vehicle allowed avoiding to deal with the challenges related to modeling human behavior. The difficulty of mathematically modeling the vehicle and the driver as two independent components rather than one unique system is due to two main reasons. First, there are numerous car models and types that make it difficult to determine the different parameters impacting the performance of the vehicle as they differ from vehicle to vehicle. Second, different driving patterns exist and the fact that they are mostly dependent on human behavior and psychology makes them very difficult to replicate mathematically. The research presented in this thesis provides a comprehensive investigation of the human-in-the-loop component in car-following theory leading to a better understanding of the human-vehicle interaction. This study was initiated due to the noticeable overlooking of driver behavior in the existing literature which, as a result, fails to capture the effect of human control and perception errors.

Human behavior plays an important role in infectious disease epidemics. The choice of preventive actions taken by individuals can completely change the epidemic outcome. Computational epidemiologists usually employ large-scale agent-based simulations of human populations to study disease outbreaks and assess intervention strategies. Such simulations rarely take into account the decision-making process of human beings when it comes to preventive behaviors. Absence of realistic agent behavior can undermine the reliability of insights generated by such simulations and might make them ill-suited for informing public health policies. In this thesis, we address this problem by developing a methodology to create and calibrate an agent decision-making model for a large multi-agent simulation, in a data driven way. Our method optimizes a cost vector associated with the various behaviors to match the behavior distributions observed in a detailed survey of human behaviors during influenza outbreaks. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations. / Master of Science / In the real world, individuals can decide to adopt certain behaviors that reduce their chances of contracting a disease. For example, using hand sanitizers can reduce an individual‘s chances of getting infected by influenza. These behavioral decisions, when taken by many individuals in the population, can completely change the course of the disease. Such behavioral decision-making is generally not considered during in-silico simulations of infectious diseases. In this thesis, we address this problem by developing a methodology to create and calibrate a decision making model that can be used by agents (i.e., synthetic representations of humans in simulations) in a data driven way. Our method also finds a cost associated with such behaviors and matches the distribution of behavior observed in the real world with that observed in a survey. Our approach is a data-driven way of incorporating decision making for agents in large-scale epidemic simulations.

Studies involving social learning have shown that social interactions are influential in directing an individual's behavior toward relatively neutral stimuli. The present study investigated the possibility that social interactions direct an individual's behavior toward aversive stimuli. Following aversion conditioning to a visual (red water) or taste (3% vinegar) CS, 80 chicks individually observed an audience of two conspecifics ingest the aversive CS or observed a nondrinking audience in the presence of the CS. Observation of a drinking audience reduced the magnitude of the aversion to the visual CS but not to the taste CS. This effect was demonstrated in latency to respond and in log intake. The differences in observational training effects found for the visual CS but not for the taste CS may have been due to differences in visual appearance between red water and vinegar. Percent intake data revealed no differences in strength of conditioning between red water and vinegar. Subjects were retested five days following the last day of initial testing. No evidence was found for observational training effects in retention. Findings were interpreted by the classical conditioning model. / Ph. D.

Typescript (photocopy). / Digitized by Kansas Correctional Industries

Master of Science / Department of Computing and Information Sciences / David A. Gustafson / IEDs are a prevailing threat to today’s armed forces and civilians. With some IEDs being well concealed and planted sometimes days or weeks prior to detonation, it is extremely difficult to detect their presence. Remotely triggered IEDs do offer an indirect method of detection as an insurgent must monitor the IED’s kill zone and detonate the device once the intended target is in range. Within the safe confines of a video game we can model the behavior of an insurgent using remotely triggered IEDs. Specifically, we can build a model of the sequence of actions an insurgent goes through immediately prior to detonating an IED. Using this insurgent model, we can recognize the behavior an insurgent would exhibit before detonating an IED. Once the danger level reaches a certain threshold, we can then react by changing our original course to a new one that does not cross the area we believe an IED to be in. We can show proof of concept of this by having human players take on the role of an insurgent in an online video game in which they try to destroy an autonomous agent. Successful tactics used by the autonomous agent should then be good tactics in the real world as well.

Motivated by challenges and opportunities in nutritional epidemiology and food journaling, ubiquitous computing researchers have proposed numerous techniques for automated dietary monitoring (ADM) over the years. Although progress has been made, a truly practical system that can automatically recognize what people eat in real-world settings remains elusive. This dissertation addresses the problem of ADM by focusing on practical eating moment detection. Eating detection is a foundational element of ADM since automatically recognizing when a person is eating is required before identifying what and how much is being consumed. Additionally, eating detection can serve as the basis for new types of dietary self-monitoring practices such as semi-automated food journaling. In this thesis, I show that everyday eating moments such as breakfast, lunch, and dinner can be automatically detected in real-world settings by opportunistically leveraging sensors in practical, off-the-shelf wearable devices. I refer to this instrumentation approach as "commodity sensing". The work covered by this thesis encompasses a series of experiments I conducted with a total of 106 participants where I explored a variety of sensing modalities for automatic eating moment detection. The modalities studied include first-person images taken with wearable cameras, ambient sounds, and on-body inertial sensors. I discuss the extent to which first-person images reflecting everyday experiences can be used to identify eating moments using two approaches: human computation, and by employing a combination of state-of-the-art machine learning and computer vision techniques. Furthermore, I also describe privacy challenges that arise with first-person photographs. Next, I present results showing how certain sounds associated with eating can be recognized and used to infer eating activities. Finally, I elaborate on findings from three studies focused on the use of on-body inertial sensors (head and wrists) to recognize eating moments both in a semi-controlled laboratory setting and in real-world conditions. I conclude by relating findings and insights to practical applications, and highlighting opportunities for future work.

The Best PhD Thesis in the Faculties of Architecture, Arts, Business & Economics, Education, Law and Social Sciences (University of Hong Kong), Li Ka Shing Prize, 1997-1999. / published_or_final_version / Philosophy / Doctoral / Doctor of Philosophy

Mate preferences provide an opportunity to explore the validity of evolutionary and social role origin theories of sex differences in human behaviour. In evolutionary models, preferences are sex-specific adaptive responses to constraints to reproductive success. In social role models, sex differences arise from the allocation of men and women to different gender roles. I explored the effects of the status of women on preferences to assess the validity of the origin theories. I developed an adequate measure of female status (i.e. resource control), and explored its effects on female preferences in an online survey (Chapter 3), a mail-shot survey (Chapter 4), and a sample of non-industrial societies (Chapter 5). Results implicated a role of constraints on women in the expression of female-typical preferences. In an experimental manipulation of female perceptions of their status, results enabled greater confidence in the attribution of causal direction to relationships (Chapter 6). In Chapter 7, I explored the conditions under which the relationships of interest occurred. In Chapter 8, to further explore the origin models I investigated the effects of resource control on the magnitudes of sex differences in preferences. In Chapter 9, I explored relationships between a characteristic more closely related to the male gender role (i.e. apparent intelligence) and femininity in female faces. Women who were considered to look more intelligent were perceived as less feminine. In Chapter 10, I investigated the effects of reproductive strategy on mate preferences. Results were consistent with evolutionary models of behaviour. I argue that “status” is a multidimensional construct, and that its effects on mate preferences are complex, that while results were generally more consistent with an evolutionary than the biosocial model, integration of models would provide greater insight into human mate preferences.