The Cognitive Systems Engineering Lab’s 25+ researchers are currently working on a plethora of projects ranging from the operating room to the air traffic control tower. We are focused on providing the most cutting-edge approach to safety, maximizing the benefits. Listed in the right column are some of the research projects that members of the lab are currently working on. Click and explore any of the pages to learn more
Human-Machine Teaming, Proactive and Anticipatory Safety, Visual Analytics, Systems Complexity Science: What do they mean
Big Brother is a study investigating the ability of a computer analytic to aid in human understanding of a full-motion view (FMV) exploitation scenario. The purpose is to ‘Evaluate Human-Machine teams for Graceful Extensibility.’ which assesses the types and magnitude of differences between human-human and human-machine team interactions in a joint sensemaking task.
This project’s objective is to develop and validate a method for identifying and supporting tactical and opportunistic collaboration strategies in human-robot systems. Complex operations such as disaster response (e.g., search and rescue tasks with robotic assets), emergency response (e.g., a fire department that sends out UAVs to support firefighters), space operations and, to a lesser degree, manufacturing (e.g., a human-robot system performing a relatively unstructured assembly task) are characterized by time pressure, uncertain demands, and conflicting goals. To enable a range of human control behaviors in human-robot collaboration, integration of human and robot capabilities needs to explicitly support tactical and opportunistic control.
OSU Testing: CSEL lab members are supporting the design and implementation of new procedures, tools, and facilities to help Ohio State maintain control over the spread of COVID-19 on campus.
Columbus Bandits: This project investigates the potential for machine algorithms to support public health initiatives. By designing and testing different means of performing COVID-19 testing, CSEL hopes to use technology to quickly and effectively direct health resources to the people that need them most.
This project’s objective is to develop methods for identifying and evaluating design characteristics of automated or complex system. Complex systems such as commercial aircraft, have many high vulnerabilities that can arise. These vulnerabilities may undermine flight crew performance when non-normal and abnormal events occur. The scope of this project is to provide the Federal Aviation Administration (FAA) with recommendations regarding the evaluation process of flight deck systems.
CSEL is exploring a new approach to testing and evaluation (T&E) of human-machine systems called joint activity testing (JAT). This methodology focuses on evaluating the performance of the joint human-machine system (i.e., joint activity) as challenge to the system increases. This methodology was designed to facilitate extrapolating insights outside the limits of discrete testing sets in order to better anticipate how complex human-machine systems will respond to scenarios that were not explicitly tested. CSEL has now begun to operationalize JAT in multiple healthcare and intelligence analysis domains spanning multiple projects and continues to further develop the methodology through these experiences.
CSEL in collaboration with Mosaic ATM is working on building a Contingency Planning Toolkit for Advanced Air Mobility. The project will use WMC (Work Models that Compute) a fast time simulator to evaluate candidate architectures to define the requirement for distributed AAM contingency Planning. We will investigate significant task load drivers like cognitive work, coordination, collaboration, and information exchange. We will use CSE (Cognitive Systems Engineering) methods to evaluate envisioned ConOps using the above factors.
NSF CAREER: Making Robots More Cooperative Agents: Controlling Costs of Coordination Through Graph-Based Models of Joint Activity
This research project develops a generalizable formalization for representing and analyzing joint activity in human-robot systems by combining theories from cognitive and social sciences with techniques from graph theory and agent-based modeling. This framework affords objective and dynamic analysis of the teamwork required to manage interdependencies between humans and robots. Based on the model, the research develops techniques for dynamically adapting and controlling coordination costs to improve collaboration and avoid coordination breakdowns.
Systemic Factors Influencing Risk Aversion: Diagnosing Behaviors and Tailoring Interventions for Lasting Transformation
This research seeks to 1) uncover the underlying, largely invisible system pressures on the acquisition workforce in the DoD that influence innovative behaviors, and 2) design interventions that address systemic contributors in order to incentivize lasting behavior changes leading to the kind of cultural change required to meet the National Defense Strategy to block Russia and China and restore America’s competitive edge.
To enable safe and scalable integration of UAM into the NASA there is a need for human-autonomy teaming that supports ad hoc coordination across a variety of operators, including Pilots in Command (PIC), UAS Service Suppliers (USS), Supplemental Data Service Providers (SDSP), and Air Traffic Management (ATM). Effective human-autonomy teaming becomes particularly critical when disturbances challenge nominal operations. The objective of this project is to characterize coordination strategies for UAM contingency management, specifically the effectiveness and appropriateness of strategies for coordination in relation to contextual factors such as workload, urgency, and common ground for various UTM/UAM roles.
Accidents and disasters – whether natural disasters, mass casualty incidents, industry accidents, or others – draw on our emergency response systems in unexpected ways. Resiliency and strategies to cope with complexity are paramount to managing effects of these disasters. Drawing on our experiences from a variety of domains, CSEL studies disaster response and consults in disaster planning.
OSU’s CSEL performed some of the first research studies on how RAS was changing the operating environment. Most notably, these projects were the first to quantify the skill and cognitive changes required success between RAS and its predecessor technique, laparoscopy. This pioneering research lead to changes in the way clinicians are trained for participation in RAS procedures as well as the development of new cooperation safety protocols for the protection of staff assisting the surgeon while the robot is in use.
As NASA-funded research, this project entailed the development of a novel process for the evaluation of the expected performance resiliency of small drones in the domestic airspace. We sought to ensure that small drones become increasingly incorporated into daily aviation activities and their system designed all for their safe operation under the widest possible range of conditions. The product technique, the resiliency trade-space analysis, can be used to help system designs and operator understand how to best deploy their limited resources to greatest effect with regard to the resilience of the system.
With the reality of drones being used commercially in large numbers almost certain, the state of Ohio began designing an unmanned air traffic management (UTM) system to ensure that it would stay on the cutting edge of technology. The lab was asked to work as experts in the Human-Machine Teaming space to work on mitigating risk in contingency management situations. In collaboration with multiple of the state’s experts in the field, the Lab worked to make sure that this will be done safely and efficiently in the state.
We research and design and clinical alarm systems that reflect the events they signal and support physician and clinician sensemaking.
Through visual analytics and human-machine teaming principles, we leverage advancing technology to improve physicians’ and clinicians’ abilities to monitor their patients.
As founders and key figures in the evolution of Cognitive Systems Engineering, we have abstracted our research into patterns of complexity and resilience that are applicable across domains to improving performance. Several of these contributions are seminal to the field.
Although all types of analytic processes, from those in scientific research to those used in legal analysis, purport to encourage high degrees of rigor, little legitimate research has been done to explore what the attributes of rigor might be or what processes are most likely to produce high quality analysis. CSEL’s rigor research was the first to do just that.