Semantic and Syntactic Interference in Sentence Comprehension: A Comparison of Working Memory Models - Tan, Yingying; Martin, Randi C.; Van Dyke, Julie A. (Frontiers in Psychology, 2017)
This study investigated the nature of the underlying working memory system supporting sentence processing through examining individual differences in sensitivity to retrieval interference effects during sentence comprehension. Interference effects occur when readers incorrectly retrieve sentence constituents which are similar to those required during integrative processes. We examined interference arising from a partial match between distracting constituents and syntactic and semantic cues, and related these interference effects to performance on working memory, short-term memory, vocabulary, and executive function tasks. For online sentence comprehension, as measured by self-paced reading, the magnitude of individuals’ syntactic interference effects was predicted by general WM capacity and the relation remained significant when partialling out vocabulary, indicating that the effects were not due to verbal knowledge. For offline sentence comprehension, as measured by responses to comprehension questions, both general WM capacity and vocabulary knowledge interacted with semantic interference for comprehension accuracy, suggesting that both general WM capacity and the quality of semantic representations played a role in determining how well interference was resolved offline. For comprehension question reaction times, a measure of semantic short-term memory capacity interacted with semantic but not syntactic interference. However, a measure of phonological capacity (digit span) and a general measure of resistance to response interference (Stroop effect) did not predict individuals’ interference resolution abilities in either online or offline sentence comprehension. The results are discussed in relation to the multiple capacities account of working memory (e.g., Martin & Romani, 1994; Martin & He, 2004), and the cue-based retrieval parsing approach (e.g., Lewis, Vasishth, & Van Dyke, 2006; Van Dyke, Johns, & Kukona, 2014). While neither approach was fully supported, a possible means of reconciling the two approaches and directions for future research are proposed.