Brain-constrained Neural Modeling Explains Fast Mapping of Words to Meaning
New Publication by Cluster Members Rosario Tomasello and Friedemann Pulvermüller
Although teaching animals a few meaningful signs is usually time-consuming, children acquire words easily after only a few exposures, a phenomenon termed »fast mapping«. Meanwhile, most neural network learning algorithms fail to achieve reliable information storage quickly, raising the question of whether a mechanistic explanation of fast mapping is possible. Here, we applied brain-constrained neural models mimicking fronto-temporal-occipital regions to simulate key features of semantic associative learning. They compared networks with prior encounters with phonological and conceptual knowledge, as claimed by fast mapping theory, and without such prior knowledge. Fast mapping simulations showed word-specific representations to emerge quickly after 1–10 learning events, whereas direct word learning showed word-meaning mappings only after 40–100 events. Furthermore, hub regions appeared essential for fast mapping, and attention facilitated it but was not strictly necessary. These findings provide a better understanding of the critical mechanisms underlying the human brain’s unique ability to acquire new words rapidly.
More information and the whole publication can be found here:
https://academic.oup.com/cercor/advance-article/doi/10.1093/cercor/bhad007/7048899