Further evidence for feature correlations in semantic memory.

Abstract

The role of feature correlations in semantic memory is a central issue in conceptual representation. In two versions of the feature verification task, participants were faster to verify that a feature (< is juicy >) is part of a concept (grapefruit) if it is strongly rather than weakly intercorrelated with the other features of that concept. Contrasting interactions between feature correlations and SOA were found when the concept versus the feature was presented first. An attractor network model of word meaning that naturally learns and uses feature correlations predicted those interactions. This research provides further evidence that semantic memory includes implicitly learned statistical knowledge of feature relationships, in contrast to theories such as spreading activation networks, in which feature correlations play no role.