We present a framework for cognitive modeling of aesthetic decision making based on dynamic prototypes. Starting point of our work is empirical evidence which shows that subjects' initial ratings of attractiveness of objects can be influenced by adapting them to new, typically more innovative objects. The framework consists of three steps: (1) Estimating an initial prototype from the ratings, (2) adapting the prototype due to the impact of the new objects, and (3) predicting the attractiveness ratings for subsequently presented object by their similarity to the adapted prototype. The framework allows representation of prototypes and objects as feature vectors containing metrical or categorial attributes or as structural representations. Within the framework, a variety of similarity measures and similarity-to-rating mappings can be explored to gain more precise insight in the cognitive processes underlying aesthetical appreciations. We instantiated the framework for a first set of data obtained in a psychological experiment. In this experiment subjects rated the attractiveness of an initial set of chairs which varied in length of the backrest and the saturation of the color. Subjects then were adapted to a new set of chairs with extreme values on both dimensions. Finally, subjects again rated the initial objects. We tested our model and obtained promising first results.