Madeleine M. McCarty
University of Utah
13 Papers
16 Citations
Madeleine M. McCarty is an academic researcher from University of Utah. The author has contributed to research in topics: Computer science & Cognition. The author has an hindex of 5, co-authored 10 publications.
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Papers
Assessing the visual and cognitive demands of in-vehicle information systems
David L. Strayer,Joel M. Cooper,Rachel M. Goethe,Madeleine M. McCarty,Douglas Getty,Francesco Biondi +5 more
TL;DR: This objective assessment suggests that many of these IVIS features are too distracting to be enabled while the vehicle is in motion, and greater consideration should be given to what interactions should be available to the driver when the vehicle was in motion.
The Challenge of Advanced Driver Assistance Systems Assessment: A Scale for the Assessment of the Human–Machine Interface of Advanced Driver Assistance Technology
Francesco Biondi,Douglas Getty,Madeleine M. McCarty,Rachel M. Goethe,Joel M. Cooper,David L. Strayer +5 more
TL;DR: The development of a scale for assessing the human–machine interfaces of 10 different assistance systems is detailed, developed through multiple iterations, and required at least two evaluators to rate each system against specific assessment items using the following 4-point scale.
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Age-Related Differences in the Cognitive, Visual, and Temporal Demands of In-Vehicle Information Systems.
Joel M. Cooper,Camille L. Wheatley,Madeleine M. McCarty,Conner J. Motzkus,Clara L. Lopes,Gus G. Erickson,Brian R. Baucom,William J. Horrey,David L. Strayer +8 more
TL;DR: In-vehicle information systems (IVIS) refer to a collection of features in vehicles that allow motorists to complete tasks (often unrelated to driving) while operating the vehicle as discussed by the authors.
Effects of Auditory Working Memory Tasks while Switching between Autonomous and Manual Driving
Madeleine M. McCarty,Kelly Funkhouser,Jonathan R. Zadra,Frank A. Drews +3 more
- 08 Sep 2016
TL;DR: In this article, the authors evaluated the differences in braking reaction times according to individual difference in working memory capacity (WMC), specifically while driving in an autonomous car simulation and engaged in a cognitively demanding task.
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