The Cognitive Engineering Center (CEC) was founded in 2005 by Dr. Amy Pritchett and is based in the College of Engineering at the Georgia Institute of Technology.
Cognitive engineering focuses on the analysis, design, and evaluation of complex socio-technical systems of people and technology such as air/ground transportation and military systems. It combines knowledge and experience from the cognitive and computer sciences, human factors, human-computer interaction, and systems engineering. Human cognitive activities such as planning, decision making, and problem solving, should be considered early in the systems design process of technology, procedures, or teams. The goals of the field are to provide better integration between human operators and the system so that human operators can act more effectively and preserve system safety and productivity if unanticipated situations arise; and to consider capabilities and limitations of human cognitive behaviors in the design processes of the system to reduce potential human errors and maximize human performance
Researchers within the CEC examine human-system integration in complex work environments from theoretical and methodological viewpoints, in the field and in the laboratory, and make substantive contributions to practice. Its research and education efforts span several domains of engineering, most notably:
MAY 10, 2017 -- A team of CEC researchers led by graduate students Martijn Ijtsma and Lanssie Ma with Profs. Amy Pritchett and Karen Feigh won the Stanley Nelson Roscoe Best Student Paper Award for their paper, “Work Dynamics of Taskwork and Teamwork in Function Allocation for Manned Spaceflight Operations” at the 2017 International Society of Aviation Psychology. The review committee stated that they particularly liked the relevance of the problem and the rigorousness of the research.
APR 5, 2017 -- Today, CEC graduate student, Marc Canellas, successfully defended his PhD in Aerospace Engineering, titled, "Decision Making with Incomplete Information." His research was advised by Professor Karen Feigh of the CEC with committee members Prof. Brian German (AE), Prof. Amy Pritchett (AE), Prof. Steve Cross (ISYE), and Prof. Juan Rogers (PUBP).
Yosef Razin presented his paper, "Learning to Predict Intent from Gaze During Robotic Hand-Eye Coordination Tasks" at the 2017 AAAI conference this week in San Francisco, CA. The work showed how accounting for anticipatory eye movements in addition to the movements of the robot improves intent estimation. This research compares the application of various machine learning methods to intent prediction from gaze tracking data during robotic hand-eye coordination tasks.
JAN 31, 2017 - How do pilots perform in the potential air traffic operations of advanced flight deck interval management and closely spaced parellel operations? Well, there's still some work to be done on all fronts - pilot training, procedures, and flight deck systems - according to a newly published study in the AIAA Journal of Air Transportation by CEC Prof. Amy Pritchett and research engineer, Rachel Haga.
JAN 27, 2017 - CEC researchers Martijn IJtsma, Lanssie Ma, Dr. Amy Pritchett, and Dr. Karen Feigh, will present a new paper on function allocation at the upcoming 2017 International Symposium on Aviation Psychology in Dayton, Ohio, USA.
JAN 9, 2017 - CEC professors, Dr. Karen Feigh and Dr. Amy Pritchett, recently received a two year, $600K NASA Early Stage Innovation (ESI) grant for "Technologies for Mixed-Initiative Plan Management for Human Space Flight." Their goal is to develop technology that will allow on-board astronauts to develop their own short- and long-term plans for accomplishing mission objectives.
DEC 30, 2016 - What information should be presented to or hidden from decision makers in order to facilitate high performance in decision tasks? In a recently accepted article to IEEE Transactions on Human-Machine Systems, "Heuristic Information Acquisition and Restriction for Decision Support," CEC researchers, Marc Canellas and Karen Feigh, contribute new rules for information acquisition and restriction which do not require reliable assessments of probabilities, cue weights, and cue values, as most normative, Bayesian methods do.