We present a new approach for modeling and analyzing route choice behavior. It is motivated by the need to reduce the complexity of the state-of-the-art choice models. It is inspired by the simplifications done by the travelers, using representations of their surrounding space. The proposed framework is based on elements designed to mimic the mental representations used by travelers, denoted as Mental Representation Items (MRIs). It allows the modeler to adjust the level of complexity according to the needs of the specific application. This paper describes how operational models based on MRIs can be derived and discusses the applications of these models to traffic assignment and route guidance systems. We report estimation results using revealed preference data to demonstrate the applicability and validity of the approach.