The first argument emphasizes learning (Bechtel and Abrahamsen 2002: 51). A vast range of cognitive phenomena involve learning from experience. Many connectionist models are explicitly designed to model learning, through backpropagation or some other algorithm that modifies the weights between nodes. By contrast, connectionists often complain that there are no good classical models of learning. Classical computationalists can respond by citing perceived defects of connectionist learning algorithms (e.g., the heavy reliance of backpropagation upon supervised training). Classical computationalists can also cite Bayesian decision theory, which models learning as probabilistic updating. More specifically, classical computationalists can cite the achievements of Bayesian cognitive science, which uses Bayesian decision theory to construct mathematical models of mental activity (Ma 2019). Over the past few decades, Bayesian cognitive science has accrued many explanatory successes. This impressive track record suggests that some mental processes are Bayesian or approximately Bayesian (Rescorla 2020). Moreover, the advances mentioned in §2 show how classical computing systems can execute or at least approximately execute Bayesian updating in various realistic scenarios. These developments provide hope that classical computation can model many important cases of learning.