A Metatheory of Classical and Modern Connectionism

Contemporary AI models owe much of their success and discontents to connectionism, a framework in cognitive science that has been (and continues to be) highly influential. Herein, we analyze artificial neural networks (ANNs): a) when used as scientific instruments of study; and b) when functioning as emergent arbiters of the zeitgeist in the cognitive, computational, and neural sciences. Building on our previous work with respect to analogizing between ANNs and cognition, brains, or behaviour (Guest & Martin, 2023), we use metatheoretical analysis techniques (Guest, 2024), including formal logic, to characterise two distinct tendencies within connectionism that we dub classical and modern, with divergent properties, e.g. goals, mechanisms, scientific questions. We also demonstrate how we, as a field, often fail to follow important lines of argument to their end --- this results in a paradoxical praxis. By engaging more deeply with (meta)theory surrounding ANNs, our field can obviate the cycle of AI winters and summers, which need not be inevitable.