To rapidly perceive, recognize, and process printed words, some well-defined organization of word units must exist in the mental lexicon. Models of visual word recognition are concerned with the principles of this organization. For example, the entry-opening model (Forster, 1999; Forster & Davis, 1984) assumes that lexical entries are organized into bins based on their orthographic form. Thus, words sharing similar letter sequences (or orthographic neighbors) would be located in the same bin. Upon presentation of a printed word, the orthographic properties of the input are used to calculate an approximate address (i.e., a bin number), and a frequency-ordered search within that bin is performed to locate the matching entry. Alternatively, attractor-based models of reading (e.g., Rueckl, 2002) assume that printed words are represented as points in a perceptual space that is defined in terms of orthographic properties. Words that are close together in this perceptual space will tend to overlap in their orthographic structure. The process of word recognition is then described in terms of a trajectory of the system through its state space, where the initial point of this trajectory is some random position in the state space and the final point is an attractor basin corresponding to the input word. Each word has a unique attractor, and the positions of the attractors in the state space are organized to reflect similarities in spelling.