The concept of attention is complex and multifaceted and can be approached from many perspectives. One such perspective is of attention as a limited pool of resources. Kahneman’s (1973) model of limited capacity provides a basis for understanding constraints on attention, including the costs of divided attention. In the same vein as Kahneman’s model, the Framework for Understanding Effortful Listening (FUEL; Pichora-Fuller et al., 2016) applies the concept of limited attentional capacity to the demands of listening in a variety of contexts. The current work examines novel combinations of the methods commonly used in the field of Cognitive Hearing Science to address questions about the nature of attention allocation when listening in noise and under the constraints of divided attention. I first combined listening in noise with a secondary continuous working memory task and measured pupillary response as an index of cognitive work and listening effort. Here, I found that listening task demands affect performance on the working memory task. The shared demands of listening and working memory were not, however, evident in the pupil dilation patterns. As a result, I followed these findings by employing a different divided attention method. With the use of a temporally discrete secondary task that either closely overlapped with the listening task or did not closely overlap, I found the same carryover effects of listening demands on secondary task. Most importantly, I found that these demands interacted and were clearly present in the pupil dilation patterns, demonstrating the importance of the timing of the task demands. Together, the studies in this thesis provide evidence that these two secondary tasks access the same attentional resources as those accessed in the primary listening task and that this overlapping demand for resources can be seen in the pupillary response. / Thesis / Doctor of Philosophy (PhD) / Listening to speech in a noisy environment is a cognitively difficult and effortful task. Attending to more than one task at a time is similarly demanding and effortful. These two kinds of tasks are assumed to use the same limited pool of cognitive resources that we have available to us. This thesis combines listening in noise with divided attention tasks to demonstrate this overlap in demands for cognitive resources using novel combinations of these kinds of tasks. Additionally, this thesis uses the pupillary response—a well-studied index of cognitive effort—to further examine the nature of these overlapping task demands. These studies found that the demands of these tasks do, in fact, overlap, and contribute evidence to the current literature supporting the underlying assumption that these two tasks, and the pupillary response as a measure of effort, are accessing the same pool of limited resources.

Objective: Listening self-efficacy refers to the beliefs, or confidence, that listeners have in their capability to successfully listen in specific situations, which may influence audiologic rehabilitation outcomes. The objective of this study was to develop and validate the Listening Self-Efficacy Questionnaire (LSEQ), which quantifies listening self-efficacy in a variety of situations where the goal of the listener is to understand speech. Study Sample: Older listeners with hearing loss (N = 169) participated in the study. Design: A factor analysis showed that the LSEQ has three subscales, with beliefs about listening capabilities relating to the following situations: (1) dialogue in quiet, (2) focusing attention on a single source, and (3) complex auditory scenes. Internal consistency reliability was excellent (Chronbach's α > .80). Results: The validity of the LSEQ was demonstrated by comparing the LSEQ scores to audiologic measures, responses on questionnaires, and to the scores for reference groups of younger and older listeners with normal hearing. Conclusion: The findings indicate that the LSEQ is a valid and reliable measure of listening self-efficacy with good potential for use in clinical and research settings.

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