Innovative numerical decision tasks, free of sensory components, will be employed to investigate decision making in binary classification problems under uncertainty. These tasks are designed to externalize the observation made by the subject as well as his cutoff point along the observation axis. In contrast to the normative approach of signal detection theory, our goal will be to develop process models of decision making which give a psychological description of how subjects do formulate decision rules rather than how they should formulate such rules. Specifically, we shall seek to describe the process whereby decision rules change as parameters of the decision problem change. A new and promising method of attack that we shall employ is the examination of which behavioral measures remain invariant as the subject attempts to keep constant the strictness of his decisions.