  This article proposes a biologically inspired neurocomputational architecture
which learns associations between words and referents in different contexts,
considering evidence collected from the literature of Psycholinguistics and
Neurolinguistics. The multi-layered architecture takes as input raw images of
objects (referents) and streams of word's phonemes (labels), builds an adequate
representation, recognizes the current context, and associates label with
referents incrementally, by employing a Self-Organizing Map which creates new
association nodes (prototypes) as required, adjusts the existing prototypes to
better represent the input stimuli and removes prototypes that become
obsolete/unused. The model takes into account the current context to retrieve
the correct meaning of words with multiple meanings. Simulations show that the
model can reach up to 78% of word-referent association accuracy in ambiguous
situations and approximates well the learning rates of humans as reported by
three different authors in five Cross-Situational Word Learning experiments,
also displaying similar learning patterns in the different learning conditions.
