The idea of the tractability cycle of research is to assess if models allow for practically feasible process implementations. This method stems from computer science, is characteristic of artificial intelligence research, and is also a fruitful method in cognitive neuroscience (van Rooij, 2008). Various models, including SIT's coding model, base their predictions on the selection of one outcome from among a highly combinatorial number of candidate outcomes, so that a naive selection method could easily require more time than is available in this universe. Such a naive selection method then is said to be intractable, no matter whether it is to be performed by computers or by brains.

As I discuss in Chapter 5, SIT's coding model does allow for a tractable implementation, however. Simplest organizations of strings can be computed via a combination of feature extraction, feature binding, and feature selection – where the binding mechanism is special in that it allows for transparallel processing by hyperstrings. This form of processing – which is feasible on classical computers – is as powerful as quantum computing promises to be, and does justice to the high combinatorial capacity and speed of perceptual organization.