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

[IN PRESS] 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 machine learning. Through this challenge, I make clear the foundations of mentalism (generativism). On these foundations, I argue against the use of computational models as theories of human cognition and for limiting their scope as proxies for theories. Counter-arguments are developed, centering on the notion that computational models, like Large Language Models (LLMs), circumvent the leap from non-linguistic to linguistic during (pre-) training. Additionally, LLMs’ learning objectives are constrained by closed-sets of language-specific data over which they operate, circumventing a test of domain-specific vs. domain-general cognitive mechanisms for language acquisition. Focuses of this argument include the format of the input, the constraints on learning, and the means of data collection. These arguments are tied into M&R’s core themes, specifically concerning the necessity of rich descriptive accounts and the theory-dependence of data-interpretation.