Investigating the effects of caffeine on executive functions using traditional Stroop and a new ecologically-valid virtual reality task, the Jansari assessment of Executive Functions (JEF©)

Objective: Caffeine has been shown to have effects on certain areas of cognition, but in executive functioning the research is limited and also inconsistent. One reason could be the need for a more sensitive measure to detect the effects of caffeine on executive function. This study used a new non-immersive virtual reality assessment of executive functions known as JEF© (the Jansari Assessment of Executive Function) alongside the ‘classic’ Stroop Colour- Word task to assess the effects of a normal dose of caffeinated coffee on executive function. Method: Using a double-blind, counterbalanced within participants procedure 43 participants were administered either a caffeinated or decaffeinated coffee and completed the ‘JEF©’ and Stroop tasks, as well as a subjective mood scale and blood pressure pre- and post condition on two separate occasions a week apart. JEF© yields measures for eight separate aspects of executive functions, in addition to a total average score. Results: Findings indicate that performance was significantly improved on the planning, creative thinking, event-, time- and action-based prospective memory, as well as total JEF© score following caffeinated coffee relative to the decaffeinated coffee. The caffeinated beverage significantly decreased reaction times on the Stroop task, but there was no effect on Stroop interference. Conclusion: The results provide further support for the effects of a caffeinated beverage on cognitive functioning. In particular, it has demonstrated the ability of JEF© to detect the effects of caffeine across a number of executive functioning constructs, which weren’t shown in the Stroop task, suggesting executive functioning improvements as a result of a ‘typical’ dose of caffeine may only be detected by the use of more real-world, ecologically valid tasks.

Competency Based Assessment Using Virtual Reality (VERT): Is It a Realistic Possibility ?

The education of the radiography profession is based within higher education establishments, yet a critical part of all radiography programmes is the clinical component where students learn the practical skills of the profession. Assessments therefore not only have to assess a student’s knowledge, but also their clinical competence and core skills in line with both Health and Care Professions Council and the Society and College of Radiographers requirements. This timely thesis examines the possibility of using the Virtual Environment for RadioTherapy (VERT) as an assessment tool to evaluate a student’s competence so giving the advantage of a standard assessment and relieving time pressures in the clinical department. A mixed methods approach was taken which can be described as a Quantitative Qualitative design with the emphasis being on the Quantitative element; a so called QUAN  qual design. The quantitative evaluation compared two simulations, one in the virtual reality environment and another in the department using a real treatment machine. Students were asked to perform two electron setups in each simulation; the order being randomly decided and so the study would be described as a randomised cross-over design. Following this, qualitative data was collected in student focus groups to explore student perspectives in more depth. Findings indicated that the performance between the two simulators was significantly different, p < 0∙001; the virtual simulation scoring significantly lower than the hospital based simulation overall and in virtually all parameters being assessed. Thematic analysis of the qualitative data supported this finding and identified 4 main themes; equipment use, a lack of reality, learning opportunities and assessment of competence. One other sub-theme identified for reality was that of the environment and senses.

Probabilistic Graphical Modelling for Software Product Lines: A Frameweork for Modeling and Reasoning under Uncertainty

This work provides a holistic investigation into the realm of feature modeling within software product lines. The work presented identifies limitations and challenges within the current feature modeling approaches. Those limitations include, but not limited to, the dearth of satisfactory cognitive presentation, inconveniency in scalable systems, inflexibility in adapting changes, nonexistence of predictability of models behavior, as well as the lack of probabilistic quantification of model’s implications and decision support for reasoning under uncertainty. The work in this thesis addresses these challenges by proposing a series of solutions. The first solution is the construction of a Bayesian Belief Feature Model, which is a novel modeling approach capable of quantifying the uncertainty measures in model parameters by a means of incorporating probabilistic modeling with a conventional modeling approach. The Bayesian Belief feature model presents a new enhanced feature modeling approach in terms of truth quantification and visual expressiveness. The second solution takes into consideration the unclear support for the reasoning under the uncertainty process, and the challenging constraint satisfaction problem in software product lines. This has been done through the development of a mathematical reasoner, which was designed to satisfy the model constraints by considering probability weight for all involved parameters and quantify the actual implications of the problem constraints. The developed Uncertain Constraint Satisfaction Problem approach has been tested and validated through a set of designated experiments. Profoundly stating, the main contributions of this thesis include the following: • Develop a framework for probabilistic graphical modeling to build the purported Bayesian belief feature model. • Extend the model to enhance visual expressiveness throughout the integration of colour degree variation; in which the colour varies with respect to the predefined probabilistic weights. • Enhance the constraints satisfaction problem by the uncertainty measuring of the parameters truth assumption. • Validate the developed approach against different experimental settings to determine its functionality and performance.