2025/10/16 by Olivia Guest, Andrea E. Martin · 2 voices · 4 citations
Neuroscience · Psychology · #Action Observation and Synchronization #Argument (complex analysis) #Artificial neural network #Cognition #Connectionism #Embodied and Extended Cognition #Field (mathematics) #Metatheory #Philosophy of science #Psychology of Moral and Emotional Judgment #Symbolic artificial intelligence
paper · doi:10.1037/rev0000591
published in Psychological Review 133(3), 719-736 (American Psychological Association)
openalex created_date 2025/10/10 · openalex publication_date 2025/10/16 · openalex updated_date 2026/07/15
Contemporary artificial intelligence 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: (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 artificial neural networks and cognition, brains, or behavior (Guest & Martin, 2023), we use metatheoretical analysis techniques (Guest, 2024), including formal logic, to characterize two distinct tendencies within connectionism that we dub classical and modern, with divergent properties, for example, goals, mechanisms, and 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 artificial neural networks, our field can obviate the cycle of artificial intelligence winters and summers, which need not be inevitable. (PsycInfo Database Record (c) 2026 APA, all rights reserved).