- Ph.D., Psychology, George Mason University, 2011
- M.A., Psychology, George Mason University, 2007
- B.S., Human Factors, Tufts University, 2003
- PADI Certified Open Water Scuba Diver
- New Investigator Award from APA Division of Experimental Psychology (Division 3), 2012
- NASA Graduate Student Researchers Program grant $30,000 annual, 2008-2011
- North American Finalist for Enhanced Safety of Vehicles automotive design competition, 2009
- Recipient of the Deflorez Prize in Human Engineering, 2003
- Human Factors and Ergonomics Society 2001-present
- Computational Modeling Technical Group
- Cognitive Engineering Technical Group
- Aerospace Technical Group
- Surface Transportation Technical Group
- Product Design Technical Group
- Society of Automotive Engineering 2011-present
- Voting member of Safety and Human Factors Steering Committee 2015-present
- President 2006-2007
- American Psychological Association 2006-present
- Division 21 - Applied Experimental and Engineering Psychology
- Association for Psychological Science 2006-2011
- Cognitive Science Society 2008-2011
Dr. Cades specializes in human factors investigations of vehicle and aircraft operator behavior, including perception response time, visual perception, nighttime visibility, and distractions and has investigated the effects of advanced driver assistance systems (ADAS) and highly automated vehicles (HAVs) on driver behavior. He also has expertise in the evaluation and development of warnings and instructions for a wide range of consumer and industrial products.
Dr. Cades utilizes his background in human factors and usability testing to support his work in these areas. He received his Ph.D. in Human Factors and Applied Cognition from George Mason University in 2011.
At Exponent, Dr. Cades has investigated vehicle operator behavior of automobiles, commercial trucks, bicycles, motorcycles, and aircraft. He has evaluated the adequacy of warnings on products and in their manuals and he has applied his experience to projects involving safety- and health- related user behaviors of industrial equipment, kitchen appliances, video game entertainment systems, consumer electronics, sports and recreation equipment, home theater products, and personal protective equipment.
Dr. Cades has expertise in the testing and analysis of how interruptions and distractions affect performance. He has investigated the negative effects of distractions in environments, including, but not limited to, driving, aviation, healthcare, offices, and classrooms. He has applied this knowledge to see how distractions can cause errors that lead to accidents. With respect to aviation, specifically, he has collected over forty hours of data from airline pilots performing safety critical flight tasks with interruptions and distractions. Dr. Cades has performed on-road evaluation of ADAS including auto-braking, collision mitigation and warning, blind spot indication, and lane departure warning.
Dr. Cades also has expertise in evaluating and designing graphical user interfaces including devices for use in automobiles and aircraft. He has previously been employed in the field of usability and user experience digital product design. He has investigated the effects of manual and voice-activated infotainment devices in automobiles. He also designed a dashboard display to assist drivers in maintaining safe speeds while driving in adverse conditions and explored how aging and glare affect people's driving ability. For commercial aircraft, he has worked with pilots, air traffic controllers, and airline operations in support of FAA's NextGen initiative.
In Dr. Cades's graduate work, he has utilized and presented on various statistical methods and has authored papers on driver behavior with respect to in-vehicle displays and devices, flight deck performance with novel systems and interruptions, the effects of glare on human vision, how attributes of interruptions affect task performance, ways to improve how people handle distractions, interruptions' effects in different environments, and various statistical approaches for predicting and understanding research outcomes.