From sound to meaning: Phonology-to-Semantics mapping in visual word recognition

Abstract

In the present study, the role of phonological information in visual word recognition is investigated by adopting a large-scale data-driven approach that exploits a new consistency measure based on distributional semantics methods. A recent study by Marelli, Amenta, and Crepaldi (2015) showed that the consistency between an orthographic string and the meanings to which it is associated in a large corpus is a relevant predictor in lexical decision experiments. Exploiting irregular mappings between orthography and phonology in English, we were able to compute a phonology-to-semantics consistency measure that dissociates from its orthographic counterpart and tested both measures on lexical decision data taken from the British Lexicon Project (Keuleers et al., 2012). Results showed that both orthography and phonology are activated during visual word recognition. However, their contribution is crucially determined by the extent to which they are informative of the word semantics, and phonology plays a crucial role in accessing word meaning.

Keywords: Distributional semantic models; Orthography-semantics consistency; Phonology-semantics consistency; Visual world recognition.