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Walker,
Bruce N.
Walker,
Bruce N.
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ItemPrototype Auditory Displays for a Fuel Efficiency Driver Interface(Georgia Institute of Technology, 2014-06) Nees, Michael A. ; Gable, Thomas M. ; Jeon, Myounghoon ; Walker, Bruce N.We describe work-in-progress prototypes of auditory displays for fuel efficiency driver interfaces (FEDIs). Although research has established that feedback from FEDIs can have a positive impact on driver behaviors associated with fuel economy, the impact of FEDIs on driver distraction has not been established. Visual displays may be problematic for providing this feedback; it is precisely during fuel-consuming behaviors that drivers should not divert attention away from the driving task. Auditory displays offer a viable alternative to visual displays for communicating information about fuel economy to the driver without introducing visual distraction.
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ItemEncoding and Representation of Information in Auditory Graphs: Descriptive Reports of Listener Strategies for Understanding Data(International Community for Auditory Display, 2008-06) Nees, Michael A. ; Walker, Bruce N.While a growing wealth of data have offered insights into the best practices for auditory display design and application, little is known about how listeners internally represent and use the information presented in auditory displays. At the conclusion of three separate studies, participants responded to an open-ended question about the strategies they used to perform auditory graphing tasks. We report a descriptive analysis of these qualitative responses. Participants' comments were coded by two raters along a number of dimensions that were chosen to represent a comprehensive set of encoding and task strategy possibilities. These descriptive analyses suggest that auditory graph listeners use a variety of strategies to cognitively represent the data in the display. Furthermore, these qualitative data offer a number of insights and questions for future research on information representation for auditory displays.
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ItemListener, Task, and Auditory Graph: Toward a Conceptual Model of Auditory Graph Comprehension(Georgia Institute of Technology, 2007-06) Nees, Michael A. ; Walker, Bruce N.Auditory graph design and implementation often has been subject to criticisms of arbitrary or atheoretical decision-making processes in both research and application. Despite increasing interest in auditory displays coupled with more than two decades of auditory graph research, no theoretical models of how a listener processes an auditory graph have been proposed. The current paper seeks to present a conceptual level account of the factors relevant to the comprehension of auditory graphs by human listeners. We attempt to make links to the relevant literature on basic auditory perception, and we offer explicit justification for, or discussion of, a number of common design practices that are often justified only implicitly or by intuition in the auditory graph literature. Finally, we take initial steps toward a qualitative, conceptual level model of auditory graph comprehension that will help to organize the available data on auditory graph comprehension and make predictions for future research and applications with auditory graphs
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ItemRelative intensity of auditory context for auditory graph design(Georgia Institute of Technology, 2006-06) Nees, Michael A. ; Walker, Bruce N.A study examined the role of relative intensity levels for auditory context in auditory graph design. Auditory graphs were designed with auditory context equally as loud as sonified data, context 9 dB more intense than data, or context 9 dB less intense than data. For a point estimation task, participants who experienced auditory graphs with more intense context performed significantly better than participants who experienced graphs with data and context equally loud. Mean differences suggest that making the context either more intense or less intense than the data improved performance as compared to the equally loud condition. We suggest that differences in the intensity of context relative to data facilitate perceptual separation of the auditory streams and thus promote ease of use with auditory graphs. Sound examples are included, and implications for auditory graph design are discussed.
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ItemBrief training for performance of a point estimation sonification task(Georgia Institute of Technology, 2005-07) Walker, Bruce N. ; Nees, Michael A.This study examined different types of brief training for a point estimation task with auditory graphs. Participants estimated the price of a stock at a specific times in a 10-hour trading day as depicted in a sonified graph of the stock price data. Forty Georgia Tech undergraduates completed a pre-test, an experimental training session, and a post-test for the point estimation task. In an extension of Smith and Walker [1], a highly conceptual, task analysis-derived method of training was compared to training paradigms that used either prompting of correct responses or feedback for correct answers during training. Two additional groups, one receiving only practice as training and another completing a filler task, were also included. Results indicate that practice with feedback for the point estimation task produced better post-test performance than all other training conditions.
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ItemAn agenda for research and development of multimodal graphs(Georgia Institute of Technology, 2005-07) Walker, Bruce N. ; Nees, Michael A.Effective multimodal graphing tools can be beneficial to both sighted and visually impaired students and scientists. However, before this can become a reality, considerable research is required on the auditory graphing components. We suggest mappings, polarities, scaling, context, and training be studied in particular. We point to previous work in these areas and make suggestions for expanded research questions. We recommend that more complex and realistic data sets be used, and that visually impaired participants play a larger role in the research. The design of multimodal graphing software should be informed by empirical findings. Effective research and useful software tools will bring a broader perspective to data analysis for all who use graphs, regardless of visual ability.