It is a common assumption that more effective decisions can be made in radar sensor processing if more "information is available. However, this is not always the case since more information may not necessarily translate to better sensor performance due to a phenomenon known as information overload. What determines the effectiveness of a decision is rarely only dependent on a single performance metric. Instead, there are often multiple performance metrics involved. This necessitates the use of cost functions for modeling a loss in decision effectiveness due to deviations from the optimal values of the performance metrics. These optimal values are also often determined in a fuzzy/subjective manner by an individual using language. Two different types of statements are looked at and modeled, namely, soft and hard constraining statements. These models, called language-based cost functions (LBCFs), are derived and their properties are explored. These can then be easily combined, via a summation, to obtain a smooth objective function. The objective function is then used to find the utility of the radar system. By altering the systems parameter values the amount of information introduced into the system can be varied and the point at which the utility of the radar system decreases due to excess information can be found. This is then regarded as the information overload point or the point at which more information causes a decrease in the utility of the system. An example of a radar system with imposed constraints is provided and its resulting information overload point is found by utilizing LBCFs.