The interaction of meaning and sound in spoken word recognition

Models of spoken word recognition vary in the ways in which they capture the relationship between speech input and meaning. Modular accounts prohibit a word's meaning from affecting the computation of its form-based representation, whereas interactive models allow activation at the semantic level to affect phonological processing. We tested these competing hypotheses by manipulating word familiarity and imageability, using lexical decision and repetition tasks. Responses to high-imageability words were significantly faster than those to low-imageability words. Repetition latencies were also analyzed as a function of cohort variables, revealing a significant imageability effect only for words that were members of large cohorts, suggesting that when the mapping from phonology to semantics is difficult, semantic information can help the discrimination process. Thus, these data support interactive models of spoken word recognition.