Brain-constrained Neural Modeling Explains Fast Mapping of Words to Meaning
New Publication by Cluster Members Rosario Tomasello and Friedemann Pulvermüller
Model structure and connectivity. A) The structure and connectivity of the 12 network areas are shown, with the perisylvian articulatory-phonological system in red/pink colors, including primary motor cortex (M1i), premotor cortex (PMi), and inferior prefrontal cortex (PFi), and the acoustic phonological system in blue, including the primary auditory cortex (A1), auditory belt (AB), and parabelt (PB). Extrasylvian regions include the dorsolateral hand-motor system in yellow/brown, consisting of the lateral prefrontal (PFL), premotor (PML), and primary motor (M1L) cortex, as well as the ventral visual stream in green, including the anterior temporal (AT), temporo-occipital (TO), and primary visual (V1) areas. Numbers refer to Brodmann areas and arrows between areas represent long distance cortico-cortical connections. B) Schematic of the areas and their connectivity structure. C) Micro-connectivity structure of one modeled excitatory »cell«, labeled e. Gray lines arching upward represent within-area excitatory links that are limited to the local neighborhood (light shaded area). Purple lines arching upward capture between-area links. The underlying gray cells represent an inhibitory cell i, which inhibits neighbors proportional to the total input it receives from the neighborhood shaded in darker purple. Figure adapted from Tomasello et al. (2018). Copyright: Constant, Pulvermüller, Tomasello
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