Neural Correlates of Verb Argument Structure Processing
- 1 November 2007
- journal article
- Published by MIT Press in Journal of Cognitive Neuroscience
- Vol. 19 (11) , 1753-1767
- https://doi.org/10.1162/jocn.2007.19.11.1753
Abstract
Neuroimaging and lesion studies suggest that processing of word classes, such as verbs and nouns, is associated with distinct neural mechanisms. Such studies also suggest that subcategories within these broad word class categories are differentially processed in the brain. Within the class of verbs, argument structure provides one linguistic dimension that distinguishes among verb exemplars, with some requiring more complex argument structure entries than others. This study examined the neural instantiation of verbs by argument structure complexity: one-, two-, and three-argument verbs. Stimuli of each type, along with nouns and pseudowords, were presented for lexical decision using an event-related functional magnetic resonance imaging design. Results for 14 young normal participants indicated largely overlapping activation maps for verbs and nouns, with no areas of significant activation for verbs compared to nouns, or vice versa. Pseudowords also engaged neural tissue overlapping with that for both word classes, with more widespread activation noted in visual, motor, and peri-sylvian regions. Examination of verbs by argument structure revealed activation of the supramarginal and angular gyri, limited to the left hemisphere only when verbs with two obligatory arguments were compared to verbs with a single argument. However, bilateral activation was noted when both two- and three-argument verbs were compared to one-argument verbs. These findings suggest that posterior peri-sylvian regions are engaged for processing argument structure information associated with verbs, with increasing neural tissue in the inferior parietal region associated with increasing argument structure complexity. These findings are consistent with processing accounts, which suggest that these regions are crucial for semantic integration.Keywords
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