This paper is a study of methods for focusing reasoning systems in the presence of uncertainty in a dynamic environment. The aim is to capture human-level behavior in selection of the interesting from myriad confounding and conflicting occurrences. Up until now, the most frequently used methods for focusing have involved the use of numerical measures in the form of utility functions or pattern matchers. The advantages of the numerical methods are their speed and transparency. The disadvantage (from the cognitive science viewpoint) is that the numerical forms are not necessarily representative of human-like thinking. These evaluator functions are often formulated in an ad hoc manner, then tested and modified on experimental cases until they meet desired levels of performance. One way of tuning the form of the functions is by feedback, stochastic learning designs. The scoring functions capture behavior over a prescribed domain but cannot adaptively vary their scope. In particular the functions are not allowed to change form to accommodate discovery; an appealing feature for a truly reactive planner. Human judgment adjusts dynamically, whereas utility function form is rigid. We propose methods to overcome the rigidity and allow the evaluator functions to handle interesting situations as they occur. Application domains considered are target prioritization for autonomous reconnaissance vehicles, and local planning of trajectories for combat aircraft.