The Cognitive Systems Lab Group, 2014

The UW-Madison Cognitive Systems Laboratory (CSL) is involved in understanding and improving the capacity of joint human-technology systems. The research in this area focuses on various theories to enable appropriate support, integration and effective use of technology within different research domains. The research work within these domains involves developing models of human performance and design principles that can support these technologies and systems. Some of the human performance domains currently involved are driving, maritime navigation, tele-operations, trust in technology, process control, and the impact of interruptions on cooperative work culture. Domains related to design principles include qualitative and quantitative methods to develop and implement privacy design guidelines. These design principles are for consumer health informatics that support patient-centered care and care coordination activities among baby boomers and older adults.

RTI driving sim

A Realtime Technologies driving simulator equipped with a Ford Fusion cab, 1 degree-of-freedom pitch motion base, and 240 degrees of computer-generated scenery (powered by six projectors, eight-foot-tall screens and several LCD monitors. The 240 degree arc of projector screens and a surround sound system simulate the visual and auditory experience of driving on-road. Movement and vibration that accompany on-road driving are produced from the motion platform.

A common theme of understanding technology-mediated attention builds upon the basic psychological concepts of attention to understand how it must be shaped so that people attend to the right thing at the right time and respond appropriately. Our goal is to be able to mediate attention and create display and control systems that enable people to work effectively with increasingly sophisticated technology.

Here at CSL, researchers learn how to conduct experiments in microworld and simulator environments such as the RTI simulation platform that consists of a cab, control loading, and visual display configurations. Researchers also learn techniques of computational cognitive engineering to model joint human-technology behavior, estimate state of the operator, and to enhance data interpretation.