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Journal of Cognitive Neuroscience

November 2014, Vol. 26, No. 11, Pages 2552-2563
(doi: 10.1162/jocn_a_00669)
@ 2014 Massachusetts Institute of Technology
Differentiating Semantic Categories during the Acquisition of Novel Words: Correspondence Analysis Applied to Event-related Potentials
Article PDF (640.41 KB)

Growing evidence suggests that semantic knowledge is represented in distributed neural networks that include modality-specific structures. Here, we examined the processes underlying the acquisition of words from different semantic categories to determine whether the emergence of visual- and action-based categories could be tracked back to their acquisition. For this, we applied correspondence analysis (CA) to ERPs recorded at various moments during acquisition. CA is a multivariate statistical technique typically used to reveal distance relationships between words of a corpus. Applied to ERPs, it allows isolating factors that best explain variations in the data across time and electrodes. Participants were asked to learn new action and visual words by associating novel pseudowords with the execution of hand movements or the observation of visual images. Words were probed before and after training on two consecutive days. To capture processes that unfold during lexical access, CA was applied on the 100–400 msec post-word onset interval. CA isolated two factors that organized the data as a function of test sessions and word categories. Conventional ERP analyses further revealed a category-specific increase in the negativity of the ERPs to action and visual words at the frontal and occipital electrodes, respectively. The distinct neural processes underlying action and visual words can thus be tracked back to the acquisition of word-referent relationships and may have its origin in association learning. Given current evidence for the flexibility of language-induced sensory-motor activity, we argue that these associative links may serve functions beyond word understanding, that is, the elaboration of situation models.