Moderational Learning: A Framework for Discovering Models and IndividualDifferences from Behavioral Data

<p>Understanding how people respond to stimuli requires models capturing both common psychological mechanisms and individual variability. Here we introduce moderational learning, a data-driven unsupervised machine-learning framework that aims to jointly discover interpretable latent dimensions representing individual variability and shared generative functions that map stimuli to responses. We implement the framework combining amortized variational inference to infer individual representations with a conditional generative decoder for predicting responses. By combining posterior analyses with symbolic regression, we identify interpretable individual-difference factors and derive explicit computational models from behavioral data. We then extend the approach to cross-domain moderational learning, allowing latent representations to be inferred from multiple behavioral tasks or data modalities. Two simulation studies show that the approach captures stimulus--response mappings and recovers underlying latent structures. As a testbed, we applied moderational learning to a behavioral dataset in which 507 participants completed four value-based decision-making tasks. In empirical analyses, moderational learning achieved higher held-out predictive performance than the comparison models and yielded exploratory within-task and cross-task latent structures, with dimensions corresponding to risk discounting, delay discounting, and bidding proclivity. Sensitivity analyses further reveal that responses in pricing tasks do not always decrease monotonically with aversive attributes. Together, these results demonstrate that moderational learning provides a data-driven framework for discovering latent behavioral structure and generative mechanisms, opening a pathway toward automated theory development in psychological science.</p>