The second argument emphasizes speed of computation. Neurons are much slower than silicon-based components of digital computers. For this reason, neurons could not execute serial computation quickly enough to match rapid human performance in perception, linguistic comprehension, decision-making, etc. Connectionists maintain that the only viable solution is to replace serial computation with a “massively parallel” computational architecture—precisely what neural networks provide (Feldman and Ballard 1982; Rumelhart 1989). However, this argument is only effective against classical computationalists who insist upon serial processing. As noted in §3, some Turing-style models involve parallel processing. Many classical computationalists are happy to allow “massively parallel” mental computation, and the argument gains no traction against these researchers. That being said, the argument highlights an important question that any computationalist—whether classical, connectionist, or otherwise—must address: How does a brain built from relatively slow neurons execute sophisticated computations so quickly? Neither classical nor connectionist computationalists have answered this question satisfactorily (Gallistel and King 2009: 174 and 265).