Marika Constant, Friedemann Pulvermüller and Rosario Tomasello have used brain-constrained neural networks to simulate key features of semantic associative learning with only a few simultaneous exposures to objects, actions and corresponding word forms. This corresponds to so-called "fast-mapping": the rapid association of symbols and meaning.

Most neural network learning algorithms fail to achieve rapid information storape quickly, raising the question of whether there can be a mechanistic explanation of fast-mapping. The researchers have compared two different networks: one with prior encounters with phonological and conceptual knowledge, as claimed by fast-mapping theory and one without.

Simulations showed that word-specific representations emerged in the network modelled after fast-mapping theory after 1-10 learning events, but only after 40-100 learning events in the direct word learning model.

These findings provide a better understanding of the critical mechanisms underlying the human brain's unique ability to acquire new words quickly.

You can read the full paper published in Cerebral Cortex here.