The Foundations of the Mentalist Theory and the Statistical Machine Learning Challenge: Comments on Matthias Mahlmann’s Mind and Rights

Matthias Mahlmann’s Mind and Rights (M&R) argues that the mentalist theory of moral cognition—premised on an approach to the mind most closely associated with generative linguistics—is the appropriate lens through which to understand moral judgment’s roots in the mind. Specifically, he argues that individuals possess an inborn moral faculty responsible for the principled generation of moral intuitions. These moral intuitions, once sufficiently abstracted, generalized, and universalized by individuals, gave rise to the idea of human rights embodied in such conventions as the Universal Declaration of Human Rights. I am sympathetic to this argument. I thus take my comments on M&R as an opportunity to preempt topical challenges to the mentalist theory arising in statistical machine learning. I make clear the foundations of mentalism (generativism) in language and morality. These foundations are used to argue against the use of computational models as theories of human cognition and to limit the scope of models as proxies for theories. Focuses include the format of the input to models, the sequence of learning trajectories, the constraints on learning, and the means of data collection. These arguments are tied back into M&R’s core themes, in particular concerning the necessity of a rich descriptive account of morality and the theory-dependence of data-interpretation.