Characterizing semantic space: Neighborhood effects in word recognition

A specification of the structural characteristics of the mental lexicon is a central goal in word recognition research. Of various word-level characte ristics, semantics remains the most resistant to this endeavor. Although th ere are several theoretically distinct models of lexical semantics with fai rly clear operational definitions (e.g., in terms of feature sharing, categ ory membership, associations, or cooccurrences), attempts to empirically ad judicate between these different models have been scarce. In this paper, we present several experiments in which we examined the effects of semantic n eighborhood size as defined by two models of lexical semantics-one that def ines semantics in terms of associations, and another that defines it in ter ms of global co-occurrences. We present data that address the question of w hether these measures can be fruitfully applied to examinations of lexical activation during visual word recognition. The findings demonstrate that se mantic neighborhood can predict performance on both lexical decision and wo rd naming.