Can the ATCOs’ cognitive states be described and classified by monitoring relevant neurometric and neurophysiological parameters? Can this classification be used for the design of adaptive interfaces?
NINA is based on the assumption that different levels of cognitive activity of the air traffic controller lead to differences in physiological activity which are observable via a combination of neurometric and neurophysiological measurements, including brain and cardiac activity. NINA will explore how to describe the ATCOs’ cognitive states by monitoring relevant physiological parameters, including brain activity. These measurements will be carried out in a laboratory setting and in an ATM simulation facility. This will ultimately allow discriminating different levels of cognitive control, such as Rasmussen’s SRK (Skills-Rules-Knowledge) taxonomy.
If successful, this can be used as an input to adaptive ATM tools which adjust the interfaces and support functions to the mental state of the air traffic controller. Whilst the use of neurometric and neurophysiological indicators is well established in laboratory settings, NINA attempts to bring these into a more operational context.
The expected results of the project are twofold: firstly, the project will assess the feasibility of using neurometric and neurophysiological indicators in order to infer levels of cognitive control of air traffic controllers; secondly, benefits and difficulties with adaptive interfaces based on such indicators will be assessed and a prototype adaptive interface will be developed.