 A second line of criticism concerns the cognitive plausibility of Take the Best (Chater, Oaksford, Nakisa, &amp; Redington 2003). Nearly all of the empirical data on the performance characteristics of Take-the-Best are by computer simulations, and those original competitions pitted Take the Best against standard statistical models (Czerlinski et al. 1999) but omitted standard machine learning algorithms that Chater, Oaksford and colleagues found performed just as well as Take the Best. Since these initial studies, the focus has shifted to machine learning, and includes variants of Take-the-Best, such as “greedy cue permutation” that performs provably better than the original and is guaranteed to always find accurate solutions when they exist (Schmitt &amp; Martignon 2006). Setting aside criticisms targeting the comparative performance advantages of Take the Best qua decision model, others have questioned the plausibility of using Take-the-Best as a cognitive model. For example, Take-the-Best presumes that cue-information is processed serially, but the speed advantages of the model translate to an advantage in human decision-making derives only if humans process cue information on a serial architecture. If instead people process cue information on a parallel cognitive architecture, then the comparative speed advantages of Take-the-Best would become moot (Chater et al. 2003).