The present project intends to comprehend the neural networks and hubs that are recruited to verbs' visual recognition, as well as deep semantic processing and meaning access. There has been an increase of studies in how nouns are represented in our brains, contributing to the understanding that psycholinguistic features can influence the recognition of written words. Nouns are easily categorized by any native speaker of any language (e.g. flowers, animals, furniture). However, verbs are more com- plex to classify into a category. The Embodied Cognition (EC) theory favored the comprehension that cognitive processing might involve the body as well. Thus, EC proposes an integration of perceptual, linguistic and motor functions (Wilson et al, 2011) and the body becomes an essential part of the cognitive constitution. Therefore, verbs are the lexical items by excelence to verify the effects of motor learning through language (Shapiro, 2011; van Dam, Rueschemeyer Harold Bekkering, 2010; Barsalou et al, 2003). Learning to read would consist in connecting areas originally from the visual system and the oral language system. As Dehaene (2012) points out, the real connectivity, the pathways and the directions distributed on time, are not known yet and is a technique not largely applied to language studies. Thus, a functional connectivity analysis conducted on EEG data might reveal the online reading network recruited to process verbs.
News published in Agência FAPESP Newsletter about the scholarship: