 Partly in response to such worries, some researchers propose an integrated cognitive computational neuroscience that connects psychological theories with neural implementation mechanisms (Naselaris et al. 2018; Kriegeskorte and Douglas 2018). The basic idea is to use neural network models to illuminate how mental processes are instantiated in the brain, thereby grounding multiply realizable cognitive description in the neurophysiological. A good example is recent work on neural implementation of Bayesian inference (e.g., Pouget et al. 2013; Orhan and Ma 2017; Aitchison and Lengyel 2016). Researchers articulate (multiply realizable) Bayesian models of various mental processes; they construct biologically plausible neural networks that execute or approximately execute the posited Bayesian computations; and they evaluate how well these neural network models fit with neurophysiological data.