Building lexical neighborhoods.

Abstract

The Neighborhood Activation Model (NAM) states that the speed and accuracy of spoken word recognition are a function of the number and nature of neighbors—or similar sounding words—activated in memory by stimulus input. In particular, the model states that spoken word recognition is a function of (1) target word frequency, (2) similarity neighborhood density, and (3) similarity neighborhood frequency. In a pretest–training –posttest design, these three factors were manipulated using nonword target words and nonword neighbors to test directly predictions of NAM. The results were only partially consistent with the model. As predicted, processing times to high‐frequency targets increased as a function of neighborhood density. However, processing times for low‐frequency targets actually decreased with increased neighborhood density. Furthermore, no significant effects of neighborhood frequency were obtained. The implications of these findings for NAM and other models of spoken word recognition are discussed.