Since rule-induction algorithms are noted for their dependence on the suitability of the representation primitives (as we have noted, § 3.2.3), the possibility exists of turning this connection round, and using the effectiveness of rule induction as a measure of merit of the representation. We should note, however, that we could fail to get good results on many grounds, only one of which is the quality of the representation. If some other problems exist (e.g., see below, § 6.4.2), there would be some limitation on the performance of the rules, and we might get only relatively small effects from changing the representation.

Hence subsidiary aims were to produce a means of preparing the data in accordance with a variety of representations, then to test the performance of a rule-induction program with the prepared data. The relative performance of rules generated following the different representations would then reflect their relative merit, and hence give some lead on the correspondence of varying representations with a supposed inherent structure of the data. In human control of a complex system, this would in turn be evidence for or against the claim that certain concepts were salient features of an operator's `mental model' of the task or the system, irrespective of whether the concepts were verbalisable or not.

Inevitably, the aims of this first experiment could not be defined more closely than this in advance, since it was not at all clear where difficulties would occur, and where progress would be halted.