An Electrophysiological Investigation into the Role of Cognitive Control in the Attentional Blink

Accuracy at reporting a second-target (T2) is reduced if it is presented within approximately 500 ms of the first target (T1) – an attentional blink (AB). Early models explained the AB in terms of attentional limitations creating a processing bottleneck such that T2 processing would be impaired while T1 processing was ongoing. Theoretical models of the AB have more recently been expanded to include the role of cognitive control. In this dissertation I propose that cognitive control, defined as the optimization of information processing in order to achieve goals, is maladapted to the dual-task conditions of the AB task in that cognitive control optimizes the T1 goal, due to its temporal proximity, at the cost of T2. I start with the concept that the role of cognitive control is to serve goals, and that how goals are conceived of and the degree of motivation associated with those goals will determine whether cognitive control will create the condition that cause the AB. This leads to the hypothesis that electrophysiological measures of cognitive control and the degree of attentional investment resulting from cognitive control modulate the AB and explain individual differences in the AB. In a series of four studies feedback-related N2 amplitude, (reflecting individual differences in the strength of cognitive control), and event-related and resting alpha frequency oscillatory activity (reflecting degree of attentional investment), are used to explain both intra- and inter-individual variability in performance on the AB task. Results supported the hypothesis that stronger cognitive control and greater attentional investment are associated with larger AB magnitudes. Attentional investment, as measured by alpha frequency oscillations, and cognitive control, as measured by the feedback-related N2, did not relate to each other as hypothesized. It is proposed that instead of a measure of attentional investment alone, alpha frequency oscillatory activity actually reflects control over information processing over time, in other words the timing of attention. With this conceptualization, various aspects of cognitive control, either related to the management of goals (feedback-related N2) or the management of attention over time to meet goals, explain variability in the AB.

Slide - Celebrity Ice Wine Event

A slide from the Inniskillin Celebrity Ice Wine event featuring: Michael Burgess, Ron Barbaro; Jonathan Welsh.

Article - "Navigation emphasized in the Governor-General's Race"

An article written by Dorothy Rungeling for the magazine Canadian Aviation. She writes about her experience during the Governor-General's Race and her win.

Article - "Fixed Wing to Whirly Bird"

An article written by Dorothy Rungeling about her experience flying a helicopter for the first time. She is instructed by Bert Ratliff of the Bell Helicopter Corp. in a Bell G2 Trooper.

Characterizing Dynamic Optimization Benchmarks for the Comparison of Multi-Modal Tracking Algorithms

Population-based metaheuristics, such as particle swarm optimization (PSO), have been employed to solve many real-world optimization problems. Although it is of- ten sufficient to find a single solution to these problems, there does exist those cases where identifying multiple, diverse solutions can be beneficial or even required. Some of these problems are further complicated by a change in their objective function over time. This type of optimization is referred to as dynamic, multi-modal optimization. Algorithms which exploit multiple optima in a search space are identified as niching algorithms. Although numerous dynamic, niching algorithms have been developed, their performance is often measured solely on their ability to find a single, global optimum. Furthermore, the comparisons often use synthetic benchmarks whose landscape characteristics are generally limited and unknown. This thesis provides a landscape analysis of the dynamic benchmark functions commonly developed for multi-modal optimization. The benchmark analysis results reveal that the mechanisms responsible for dynamism in the current dynamic bench- marks do not significantly affect landscape features, thus suggesting a lack of representation for problems whose landscape features vary over time. This analysis is used in a comparison of current niching algorithms to identify the effects that specific landscape features have on niching performance. Two performance metrics are proposed to measure both the scalability and accuracy of the niching algorithms. The algorithm comparison results demonstrate the algorithms best suited for a variety of dynamic environments. This comparison also examines each of the algorithms in terms of their niching behaviours and analyzing the range and trade-off between scalability and accuracy when tuning the algorithms respective parameters. These results contribute to the understanding of current niching techniques as well as the problem features that ultimately dictate their success.