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meeting certain “esthetic principles”, lend themselves to answering unanticipated questions or predicting
the consequences of novel situations. They contended that using such mental models, which they termed
robust, is characteristic of expert behavior.
De Kleer and Brown’s theory defines mental models as topologies of submodels that represent
components of the system. Each submodel is a collection of rules that describe the causal behavior of a
component. Robustness arises out of the component models. The better a mental model’s component
models meet the following principles, the more robust the overall model is.
• The no-function-in-structure principle: the rules that specify the behavior of a system component
are context free. That is, they are completely independent of how the overall system functions. For
instance, the rules that describe how a switch in an electric circuit works must not refer, not even
implicitly, to the function of the whole circuit. This is the most central of the principles that a
robust model must follow.
• The locality principle: the rules that specify the behavior of a system component are represented
only in terms of the internal aspects of the component and its connections to other components,
not in terms of the internal aspects of other components. For instance, the rules that describe a
switch in an electrical circuit must not depend on the internal state of any other component in the
circuit. The locality principle helps ensure that the no-function-in-structure principle is met.
• The weak causality principle: the rules of the mental model attribute each event in the system to a
direct cause. The reasoning process involved in determining the next state does not depend on any
“indirect arguments”. For instance, what happens next to a component in an electrical circuit must
be directly attributable to some local cause rather than indirectly inferred by elaborately reasoning
about other components. The weak causality principle is important for the efficient running of the
mental model.
• The deletion principle: the mental model should not predict that the system will work properly even
when a vital component is removed.
An overarching aspect of robust models is that the components of the model are understood in terms of
general knowledge that pertains to those components rather than specific knowledge that pertains to the
particular configuration of the components. A non-robust model may serve for mental simulations of a
particular system under normal circumstances. A robust model is needed for transferring the knowledge
embodied in a mental model to a similar but novel problem. A robust model is also needed to mentally
simulate exceptional situations such as when a component malfunctions or a change to the system is
either made or planned. This makes robust models highly desirable.
Wickens and Kessel: transferable models through active learning
Wickens and Kessel’s work (see Kessel and Wickens, 1982; Wickens, 1996; Schumacher and Czerwinski,
1992) provides another perspective on mental model formation. They studied the performance of people
trained alternatively as monitors, who supervise a complex technological system, or as controllers, who
control the system manually. As one would expect, Wickens and Kessel found that training in system
monitoring improves people’s monitoring skills, and training in controlling a system improves controlling
skills. However, and significantly, they also found that the controllers could transfer their skills to
monitoring tasks, while the reverse was not true of the monitors. The controllers were also found to
be better at detecting system faults from subtle cues that escaped the attention of the monitors. Wickens
and Kessel inferred that the two kinds of training led to different kinds of internal models being formed.
Process control researchers evoke worrying images of supervisors of automated nuclear power plants
trained to monitor rather than to control, and of airplane pilots whose training is excessively based on
autopiloting.
Wickens and Kessel’s results are important as they show how people doing similar yet different tasks
on the same system develop different kinds of mental representations and different kinds of expertise. In
particular, a more passive task resulted in worse learning. This is not the last time that we will run into
this thought on these pages.
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