Signal detection theory measures the ability for a user to differentiate a specific signal from noise. For example, an air traffic controller needs to be able to differentiate an airplane (specific signal) from large clouds (an example of a potential noise).
Automation has the potential to be incorporated into signal detection theory. If there is an environmental hazard, then a given system has a mechanism to pick up this hazard (usually via sensors). Next, these sensors can either go straight toward automation or to the human by alerting them of the environmental hazard. For example, in an AC system, a thermostat will sense a temperature change from within the set temperature and then will signal to the system that it needs to work in order to increase or decrease the temperature. If this system did not work via automation, then the AC system would have to alert the user of the temperature change via its display and then require the user to take action in order to activate the AC system. This would be very cumbersome if AC systems worked in this manner.
In addition, there are four different outcomes from specifying a signal versus noise. The user can correctly identify a signal that is present, the user can identify a lack of a signal that is present, the user can identify a signal that is not there (false positive), and the user cannot identify a signal that is there (false negative). Let’s think about this within the scope of the medical field. If there were a doctor performing an ultra-sound looking for free fluid in the abdomen, then the doctor could:
– correctly identify free fluid in the abdomen
– correctly identify a lack of free-fluid in the abdomen
– identify free-fluid in the abdomen that is not there (false positive)
– fail to identify free-fluid in the abdomen in the abdomen (false negative)
With regards to which outcome is the most dangerous, the false negative has the potential to cause serious harm. This is because the false negative leads to the doctor missing a potential catastrophic diagnosis.
Signal detection theory is an important aspect when designing systems because it ensures that systems are designed with the idea that humans are imperfect. There are a range of factors that can affect a user’s ability to detect a signal—like level of fatigue, physical abilities, and environment—so by designing systems that accurately alert users, we are able to increase the safety of these systems.