English phonotactic learning is modeled by means of the PHACTS algorithm, a topo-logical neuronal receptive field implementing a phonotactic activation function aimed at capturing both local (i.e., phonemic) and global (i.e., word-level) similarities among strings. Limits and merits of the model are presented.
The paper investigates the morphological impact of quantitative properties of lexical and sublexical structures in the decomposition of morphologically complex words by means of an activation-based simulation. A complex nucleus of blind-to-semantics relationships turns out to allow the emergence of proto-morphological representations and to provide a… (More)
This paper investigates the processing of Italian affixed forms differing for morphotactic transparency. A lexical decision task with immediate priming was used. Following the principles of morphotactic transparency and Natural Morphology, the priming effect was hypothesized to be stronger for items with a higher degree of morphotactic transparency.… (More)