Work now spans behavior, neurons, and mathematical models. Experiments show animals create abstract task representations, and recordings from prefrontal circuits reveal patterns that flag when rules have shifted. Computational models give language to those patterns, showing how representations can emerge, compress information, and trigger searches for new rules when outcomes no longer match expectations.
Understanding these mechanisms offers a path from cellular circuits to everyday strengths and vulnerabilities of thinking. If we can map how rule discovery breaks down, we can better explain cognitive rigidity in conditions such as schizophrenia and design interventions that support flexible thought. Follow the article to explore the models and data that bridge neurons and behavior, and imagine what restoring flexible rule-making could mean for learning, work, and inclusion.
Cognitive flexibility allows us to adjust behavior when environmental rules change and to recombine prior knowledge in novel situations. However, the computations underlying rule discovery remain unclear: how do rule representations emerge as we interact with the environment? While it is often considered a hallmark of human behavior, converging evidence highlights comparable forms of abstract learning in rodents. Across species, prefrontal activity patterns encode structured representations that support generalization and monitor for rule switches. In this review, we synthesize recent behavioral, neural, and computational work highlighting models that formalize rule inference and creation. We consider how a translational framework linking circuit-level mechanisms to computational principles can help us understand disruptions in cognitive flexibility in disorders such as schizophrenia.
