The salience of consonants and vowels in learning an artificial lexicon: The effects of noise

We examined the effects of acoustic noise on the relative weightings of different segment types during lexical learning and lexical retrieval. We designed an artificial lexicon consisting of 16 CVCV words, learned as names for blackand-white shapes. For each word (dabo), another word shared the same consonants (dubeI) and a third had the same vowels (gapo). In a 4AFC task, 4 pictures were presented and the name of one picture was spoken. The participant was asked to click on that picture. On each trial, one picture had a name with the same vowels or same consonants as the target picture. Whether words were learned and tested with or without noise (3 dB SNR), overall error rates were roughly equivalent, but vowel-matched errors were much higher in the noise conditions than in the clear conditions. These results support a mechanism of adaptive plasticity whereby acoustic/phonetic cues are re-weighted to maximize the intelligibility of words during lexical learning and lexical retrieval.