Learning new words with rich and poor meaning during reading: Electrophysiological evidence

Learning new words with rich and poor meaning during reading: Electrophysiological evidence

First Author: Roberto Ferreira -- Pontificia Universidad Católica de Chile
Additional authors/chairs: 
Patricia Román; Ton Dijkstra
Keywords: Word Learning, EEG, ERP, N400, Semantics
Abstract / Summary: 

Purpose:
Words can have rich or poor meanings. The richer the meaning of a word the faster its processing, as revealed in naming and lexical decision tasks. While the effect of semantic richness on familiar word processing is well-established, very little is known about the impact of semantic richness on word learning during reading. In the present study, we investigated the acquisition of novel words imbedded in sentences with many and few semantic features over successive presentations.
Method:
Twenty-three proficient Dutch-English speakers were presented with English nonwords (e.g., hoaf, luspy) followed by a sentence that described their meaning using few semantic features (e.g., is hard and brown) or many (e.g., is used for holding, heating and boiling water for making tea and coffee). Participants had to read each novel word, followed by a different sentence, seven times with simultaneous EEG-recordings.
Results:
The number of semantic features (NoSF) associated with the novel words modulated word learning as reflected in N400 amplitude. Differences in the amplitude of the N400 component were observed after the third presentation of the novel words in sentences. There was a higher N400 for words associated with many semantic features in comparison to words with few features.
Conclusions:
This might reflect more effortful processing, harder access to meaning, or increased difficulty during semantic integration. These findings suggest that semantic richness, as measured using NoSF, does not play a facilitatory role during novel word learning, but rather increases the challenge for the brain to map new word forms onto available semantic features.