New York University
Unpacking the neural basis of reference resolution: MEG and EEG evidence
We used EEG and MEG data to investigate the neural basis of reference resolution. While language comprehension is generally associated with a network of perisylvian regions, an emerging body of evidence suggests that accessing linguistic meaning also activates modality-specific brain systems. Consequently, reference resolution might entail activation of modality-specific brain regions associated with representations of the referential domain. With EEG we showed that reference resolution in a visual display is associated with a response that is sensitive to the spatial location of the referent, indicating the involvement of a modality-specific, visual or spatial representation. On the other hand, studies of coherent language and referential ambiguity imply that additional brain regions are recruited for referential processing. With source localized MEG, we searched for regions that respond to reference resolution irrespective of the modality of the referential domain. Across 4 experiments, using both auditory and visual referential domains, we found increased activation associated with reference resolution in medial parietal cortex, and ruled out multiple alternative explanations. Research on memory has associated this region with successful episodic recollection. This suggests a parallel between reference resolution and item memory retrieval, and possibly a cognitive mechanism that is shared between linguistic and non-linguists tasks.
I am a doctoral student in Psychology with a concentration in Cognition and Perception at New York University. My dissertation research is conducted at the Neuroscience of Language lab and advised by Liina Pylkkänen. My research interests include the neural and cognitive basis of language comprehension in context. More specifically, my work examines the neural correlates of reference resolution in simple definite noun phrases using EEG and source localized MEG data. I also have a special interest in data analysis with Python and have contributed to open source data analysis software, in particular to mne-python.