Classical computationalists typically reply that it is premature to draw firm conclusions based upon biological plausibility, given how little we understand about the relation between neural, computational, and cognitive levels of description (Gallistel and King 2009; Marcus 2001). Using measurement techniques such as cell recordings and functional magnetic resonance imaging (fMRI), and drawing upon disciplines as diverse as physics, biology, AI, information theory, statistics, graph theory, and dynamical systems theory, neuroscientists have accumulated substantial knowledge about the brain at varying levels of granularity (Zednik 2019). We now know quite a lot about individual neurons, about how neurons interact within neural populations, about the localization of mental activity in cortical regions (e.g. the visual cortex), and about interactions among cortical regions. Yet we still have a tremendous amount to learn about how neural tissue accomplishes the tasks that it surely accomplishes: perception, reasoning, decision-making, language acquisition, and so on. Given our present state of relative ignorance, it would be rash to insist that the brain does not implement anything resembling Turing computation.