Scaleable Multitasking Microworlds

Research on human cognition in dynamic human-machine systems has to cope with a multitude of difficulties that stem from the domain. Complexity and dynamics of the task are nearly unlimited sources of variance that can be hardly controlled in real live situations. Experimentation with classical stimuli that can be easily provided in the lab, on the other hand, they may suffer from a lack of ecological validity and results are hardly transferable back to the domain.

Microworlds are promissing experimentational tools, that fill the gap between the before-mentioned extremes. Looking at microworlds from the point of real systems, they appear as reduced and simplified model environments that have some aspects in common with the real world. Coming from the lab-side, they might be interpreted as overly rich stimuli.

The following table provides a collection of microworlds that nicely illustrates this statement. The most simple microworld reflects only the most important attributes of a real world control solution. System dynamics, interaction patterns and information processing is largely simplified, however trained perceptional strategies can be reused. On the other end of the continuum one can find a microworld, that provides a high fidelity emulation of interaction principles of the operator console of a selected industrial scale control system.