OrBEAGLE: Integrating orthography into a holographic model of the lexicon

Abstract

Many measures of human verbal behavior deal primarily with semantics (e.g., associative priming, semantic priming). Other measures are tied more closely to orthography (e.g., lexical decision time, visual word-form priming). Semantics and orthography are thus often studied and modeled separately. However, given that concepts must be built upon a foundation of percepts, it seems desirable that models of the human lexicon should mirror this structure. Using a holographic, distributed representation of visual word-forms in BEAGLE [12], a corpus-trained model of semantics and word order, we show that free association data is better explained with the addition of orthographic information. However, we find that orthography plays a minor role in accounting for cue-target strengths in free association data. Thus, it seems that free association is primarily conceptual, relying more on semantic context and word order than word form information. © 2011 Springer-Verlag.