Limits to human optimization in inspection performance

As inspection moves from unaided human skills to human–computer hybrid tasks, there is a need for models of the human and the computer which have common parameters. With appropriate models, functions can be allocated to produce optimal designs, and assistance provided to the human inspector via job aids and training. A model was developed of the human in a two-component compound inspection task consisting of search and decision. Optimizing this model showed that the choice of optimal values of parameters in the two submodels was independent. Ten subjects were tested on a two-component inspection task, using components which had earlier been validated separately. Subjects showed some aspects of optimum behaviour, for example sub-model independence, stopping the search after an integral number of scans, and varying their decision criteria to respond to the probability and cost structure. However, in this more complex task, subjects often reverted to simpler decision rules, for example always stopping the search after one scan or accepting (or rejecting) all potential defects detected. The implication for hybrid automation systems is that humans will need help such as job aids or training if they are to perform optimally when given both search and decision tasks.