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Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition
Published on: July 13, 2019
Ventral and dorsal streams for choosing word order during sentence production
Malathi Thothathiri1, Michelle Rattinger2
1Department of Speech and Hearing Science, The George Washington University, Washington, DC 20052 malathi@gwu.edu.
Abstract:
Proficient language use requires speakers to vary word order and choose between different ways of expressing the same meaning. Prior statistical associations between individual verbs and different word orders are known to influence speakers' choices, but the underlying neural mechanisms are unknown. Here we show that distinct neural pathways are used for verbs with different statistical associations. We manipulated statistical experience by training participants in a language containing novel verbs and two alternative word orders (agent-before-patient, AP; patient-before-agent, PA). Some verbs appeared exclusively in AP, others exclusively in PA, and yet others in both orders. Subsequently, we used sparse sampling neuroimaging to examine the neural substrates as participants generated new sentences in the scanner. Behaviorally, participants showed an overall preference for AP order, but also increased PA order for verbs experienced in that order, reflecting statistical learning. Functional activation and connectivity analyses revealed distinct networks underlying the increased PA production. Verbs experienced in both orders during training preferentially recruited a ventral stream, indicating the use of conceptual processing for mapping meaning to word order. In contrast, verbs experienced solely in PA order recruited dorsal pathways, indicating the use of selective attention and sensorimotor integration for choosing words in the right order. These results show that the brain tracks the structural associations of individual verbs and that the same structural output may be achieved via ventral or dorsal streams, depending on the type of regularities in the input.
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