levels of automation in a binary categorization task
Motivation -- To study the effect of levels of automation on binary categorization decisions. Research approach -- A laboratory experiment was conducted on 80 students, employing a simulated production control task that involved binary categorizations of situations. Findings/Design -- The performance with the lower level of automation tended to be less affected by the quality of the aid and overall better than performance with the higher level of automation. Research limitations/Implications -- The system is fairly abstract, and additional validation of the findings in more realistic settings may be desirable. Originality/Value -- The study is one of a fairly small number of empirical studies on the effect of levels of automation on performance. Take away message -- Lower levels of automation may actually lead to better results in a wide range of conditions.
levels of automation binary categorization decisions signal detection