Pain expressiveness and altruistic behavior

Predictions which invoke evolutionary mechanisms ar e hard to test. Agent-based modeling in artificial life offers a way to simulate behaviors and interac tions in specific physical or social environments o ver many generations. The outcomes have implications fo r understanding adaptive value of behaviors in context. Pain-related behavior in animals is communicated to other animals that might protect or help, or might exploit or predate. An agent-based model simulated the effects of displaying or not displaying pain (expresser/non-expresser strategies) when injured, and of helping, ignoring or exploiting another in pain (altruistic/non-altruistic/selfish strategies) . Agents modeled in MATLAB interacted at random while foraging (gaining energy); random injury inte rrupted foraging for a fixed time unless help from an altruistic agent, who paid an energy cost, speeded recovery. Environmental and social conditions also varied, and each model ran for 10,000 iterations. Findings were meaningful in that, in general, conti ngencies evident from experimental work with a variety of mammals, over a few interactions, were r eplicated in the agent-based model after selection pressure over many generations. More energy-demandi ng expression of pain reduced its frequency in successive generations, and increasing injury frequ ency resulted in fewer expressers and altruists. Allowing exploitation of injured agents decreased e xpression of pain to near zero, but altruists remained. Decreasing costs or increasing benefits o f helping hardly changed its frequency, while increasing interaction rate between injured agents and helpers diminished the benefits to both. Agent- based modeling allows simulation of complex behavio urs and environmental pressures over evolutionary time.

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.