Spoken word recognition and serial recall of words from the giant component and words from lexical islands in the phonological network.

摘要

Network science is a field that applies mathematical techniques to study complex systems, and the tools of network science have been used to analyze the phonological network of language (Vitevitch, 2008). The phonological network consists of a giant component, lexical islands, and several hermits. The giant component represents the largest connected component of the network, whereas lexical islands constitute smaller groups of words that are connected to each other but not to the giant component. To determine if the size of the network component that a word resided in influenced lexical processing, three psycholinguistic tasks (word shadowing, lexical decision, and serial recall) were used to compare the processing of words from the giant component and word from lexical islands. Results showed that words from lexical islands were more quickly recognized and more accurately recalled than words from the giant component. These findings can be accounted for via a spreading activation framework. Implications for models of spoken word recognition and network science are also discussed.