Psychological Flow In A Physical Task

Psychological flow describes the mental phenomenon that takes place during intense engagement with a task (Jackson & Csikszentmihalyi, 1999). Its components have been operationalized through the development of the Flow State Scale (Jackson & Eklund, 2002). As feedback has been shown to be a critical element for the facilitation of a flow experience (Moneta, 2012), the current study sought to investigate the effect of differential feedback on psychological flow outcomes using the FSS as the dependent variable. The feedback manipulation featured three experimental groups; control, positive, and negative. This study also accounted for the personality trait of perfectionism as a variable influencing the experience of flow. Following the completion of a personality measure, participants engaged in a bolt threading task for ten minutes, then reported the time they perceived to have spent on the task as well as the outcome of their flow experience. The feedback conditions were created by the use of different size containers for participants to place their nut and bolt pairs in, and thus feedback was inherent in the task. The study found that feedback played an important role in the outcome of a flow experience. The positive feedback condition was more conducive to flow than the negative feedback condition. Furthermore, those in the positive condition outperformed those in the negative condition during the ten minutes. Goal clarity and feedback clarity differed significantly across feedback manipulations. Perfectionism¿s impact on the outcome of flow was more pronounced in the negative feedback condition than the positive or control conditions. In settings where engagement and performance are imperative, ample attention should be given to the feedback processes present in the situation.

Assessing the Validity of Brain Glucose Concentration Measurement Using Microdialysis

Brain functions, such as learning, orchestrating locomotion, memory recall, and processing information, all require glucose as a source of energy. During these functions, the glucose concentration decreases as the glucose is being consumed by brain cells. By measuring this drop in concentration, it is possible to determine which parts of the brain are used during specific functions and consequently, how much energy the brain requires to complete the function. One way to measure in vivo brain glucose levels is with a microdialysis probe. The drawback of this analytical procedure, as with many steadystate fluid flow systems, is that the probe fluid will not reach equilibrium with the brain fluid. Therefore, brain concentration is inferred by taking samples at multiple inlet glucose concentrations and finding a point of convergence. The goal of this thesis is to create a three-dimensional, time-dependent, finite element representation of the brainprobe system in COMSOL 4.2 that describes the diffusion and convection of glucose. Once validated with experimental results, this model can then be used to test parameters that experiments cannot access. When simulations were run using published values for physical constants (i.e. diffusivities, density and viscosity), the resulting glucose model concentrations were within the error of the experimental data. This verifies that the model is an accurate representation of the physical system. In addition to accurately describing the experimental brain-probe system, the model I created is able to show the validity of zero-net-flux for a given experiment. A useful discovery is that the slope of the zero-net-flux line is dependent on perfusate flow rate and diffusion coefficients, but it is independent of brain glucose concentrations. The model was simplified with the realization that the perfusate is at thermal equilibrium with the brain throughout the active region of the probe. This allowed for the assumption that all model parameters are temperature independent. The time to steady-state for the probe is approximately one minute. However, the signal degrades in the exit tubing due to Taylor dispersion, on the order of two minutes for two meters of tubing. Given an analytical instrument requiring a five μL aliquot, the smallest brain process measurable for this system is 13 minutes.