Deeply Rational

knowledge from experience via the operation (and cooperation) of active cognitive faculties embodying domain-general inductive biases. As a “to-do” list for machine learning, the DoGMA recommends that we model these faculties using architectures featuring multiple, semi-independent neural network modules characterized by distinctive wiring motifs or learning algorithms. My use of the term “module” here should be explicitly distinguished from Fodor’s use of the term (Fodor 1983), as Fodorian modules are characteristically domain-specific and strictly informationally encapsulated; though the modules of the DoGMA share other properties in common with Fodor’s list, such as performing their own distinctive operations, exhibiting a greater degree of internal than external information-sharing, and possessing a fixed neural architecture.30 These modules may, but need not, correspond to neuroanatomically distinct brain regions; indeed, we may expect that while these faculties perform distinct psychological functions, they compete for shared resources in the brain and their neural correlates might spatially overlap and even share many components.31 Once articulated, we will find that the project of modeling distinct faculties with distinct 30 I thus invoke here a particularly weak notion of a “module,” even by permissive recent standards (Robbins 2009). The notion invoked here is closest to that recommended by Carruthers (2006), though he also emphasizes the domainspecificity that I here explicitly reject. I take no stance here on whether the mind is massively modular or also contains learned, domain-specific modules—I only here suggest that a useful way forward in deep learning research involves explicitly theorizing about computational models with distinctive operations that are more internally than externally connected and which implement roles attributed to domain-general psychological faculties in a coherent cognitive architecture. 31 While I will introduce the faculties as distinct modules, we may decide upon reflection that one of the faculties can be reduced to a mode of or interaction amongst the others, or that the operations of two faculties are so intermingled that drawing a distinction between them would be artificial. Indeed, some theorists have argued that memory and imagination are merely two different aspects of the same faculty, or that attention is merely a mode of the other faculties (De Brigard 2014; Mole 2011). Debates about the exact taxonomy of the faculties could reflect a healthy maturation of empiricist theorizing without threatening the basic point that empiricists can appeal to domain-general mental operations which exceed the most basic forms of associative learning. The DoGMA does stand in opposition to more radical forms of empiricism which seek to eliminate traditional faculty taxonomies entirely, such as the eliminativism recommended by neuroscientist Luiz Pessoa (Pessoa, Medina, and Desfilis 2022). While Pessoa suggests that the standard list of faculties Deeply Rational Machines – Final Manuscript Version – Cameron Buckner cjbuckner@uh.edu 29 neural network architectures is already well underway in deep learning research, and that it is this moderate empiricist DoGMA—rather than behaviorism—which has inspired and is correspondingly bolstered by its recent achievements. Even nativists and more radical empiricists who ultimately reject the DoGMA may benefit by considering its charitable characterization and defense. In fact, nativist-leaning readers may find more points of agreement with prominent deep learning theorists than they would have otherwise expected. For one, they may find many ideas traditionally championed by nativists—such as modularity (albeit, alas, not the domainspecific or massive flavors championed by Fodor 1983), model-based learning (albeit, alas, without innate concepts), and compositionality (albeit, alas, without unlimited productivity)—vindicated by the DoGMA. For another, most nativists may happily borrow from more powerful learning algorithms and architectures to integrate the recent achievements of deep learning into their own theories (as recommended even by critics such as Marcus). Charitable consideration of the DoGMA may even help nativists marshal empirical evidence as to exactly which ideas or concepts must be supposed innate. Thus, hopefully even nativists will see the value of systematically engaging with the considered, charitably construed views of the most influential empiricists. I aim to show how prominent empiricist approaches are more reasonable and powerful than many have supposed, and I will consider the book a success if readers leave feeling that it is an open empirical question whether innate domain-specific structures are required for human-like AI, rather than being obviously required. Questioning the strength of the evidence for prominent nativist hypotheses like Core Knowledge is one route to this goal, but in the interests of space and novelty, I here focus on illustrating how much cognitive flexibility can be derived from domain-general structure in recent deep learning models, especially since much of this evidence is new to the debate and the pace of research has been so blisteringly