Bybee and McClelland (2005) have noted how this gradience resembles the patterns learned by connectionist networks (and statistical machine learning algorithms in general).
they argue that such architectures are sensitive to both local (contextual) information and general patterns, as well as to their frequency of use, similarly to natural languages.