Modeling N400 amplitude using vector space models of word representation


We use a vector space model (VSM) to simulate semantic relatedness effects in sentence processing, and use this connection to predict N400 amplitude in an ERP study by Federmeier and Kutas (1999). We find that the VSM-based model is able to capture key elements of the authors' manipulations and results, accounting for aspects of the results that are unexplained by cloze probability. This demonstration provides a proof of concept for use of VSMs in modeling the particular context representations and corresponding facilitation processes that seem to influence non-cloze-like behavior in the N400.

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