Description/Abstract/Artist Statement
Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term memory with a capacity limit that is dependent on attention and other executive functions. This study uses machine learning algorithms to find an objective relationship between participant eye tracking measurements and their responses on the NASATLX which determines subjective workload. The dataset used in this study was collected and published by researchers at the University of Windsor and publicly available.
Faculty Advisor/Mentor
Sampath Jayarathna
College Affiliation
College of Sciences
Presentation Type
Poster
Disciplines
Artificial Intelligence and Robotics | Cognitive Psychology | Data Science
Session Title
Poster Session
Location
Learning Commons @ Perry Library
Start Date
3-19-2022 9:00 AM
End Date
3-19-2022 11:00 AM
Upload File
wf_yes
Included in
Artificial Intelligence and Robotics Commons, Cognitive Psychology Commons, Data Science Commons
Objective Measure of Working Memory Capacity Using Eye Movements
Learning Commons @ Perry Library
Human-autonomy teaming (HAT) has become an important area of research due to the autonomous systems being developed for different applications, such as remotely controlled aircraft. Many remotely controlled vehicles will be controlled by automated systems, with a human monitor that may be monitoring multiple vehicles simultaneously. The attention and working memory capacity of operators of remote-controlled vehicles must be maintained at appropriate levels during operation. However, there is currently no direct method of determining working memory capacity, which is important because it is a measure for how memory is being stored for a short term and interacting with long term memory with a capacity limit that is dependent on attention and other executive functions. This study uses machine learning algorithms to find an objective relationship between participant eye tracking measurements and their responses on the NASATLX which determines subjective workload. The dataset used in this study was collected and published by researchers at the University of Windsor and publicly available.