Characterizing semantic space: Neighborhood effects in word recognition

The findings demonstrate that semantic neighborhood can predict performance on both lexical decision and word naming, and two models of lexical semantics that define semantics in terms of associations and global co-occurrences are examined.

Abstract

A specification of the structural characteristics of the mental lexicon is a central goal in word recognition research. Of various word-level characteristics, semantics remains the most resistant to this endeavor. Although there are several theoretically distinct models of lexical semantics with fairly clear operational definitions (e.g., in terms of feature sharing, category membership, associations, or cooccurrences), attempts to empirically adjudicate between these different models have been scarce. In this paper, we present several experiments in which we examined the effects of semantic neighborhood size as defined by two models of lexical semantics—one that defines semantics in terms of associations, and another that defines it in terms of global co-occurrences. We present data that address the question of whether these measures can be fruitfully applied to examinations of lexical activation during visual word recognition. The findings demonstrate that semantic neighborhood can predict performance on both lexical decision and word naming.