In an attractor model of semantic memory, semantic similarity is determined by degree of featural overlap. In contrast, in spreading activation theory, two concepts are similar if they share features or if they are linked to the same superordinate category node. We present an attractor network model of computing word meaning and use it to simulate the data of McRae and Boisvert (in press), who found that short SOA semantic similarity priming directly depends on degree of featural overlap. The two accounts of semantic similarity are then contrasted in a human experiment. In support of attractor networks, priming effects were determined by featural overlap, and no evidence was found for priming through a purported superordinate node. It is concluded that lexical concepts are not represented as static nodes in a hierarchical system.

When we encounter a person we rapidly form an impression. This impression is shaped by factors inherent to the person and event, including gender cues, skin colour, clothing, and context, along with pre-existing knowledge about the world. We present a PDP model of impression formation and person knowledge that has roots in the study of semantic cognition. We trained our model to learn about people of different races, genders, and occupations (e.g., black female doctor), along with context related to those occupations (e.g., hospital bed), and semantic attributes related to those occupations (e.g., intelligent). We demonstrate and explain the interesting finding that the model encodes knowledge about gender and race in early ‘visual’ hidden layers, despite no visual similarity being built-in, and no feedback connections from ‘semantic’ regions. This suggests a third mechanism through which the knowledge we hold about people may quite literally shape the way we see them.