Discovering Cognitive Strategies with Tiny Recurrent Neural Networks

Normative modeling frameworks such as Bayesian inference and reinforcement learning provide valuable insights into the fundamental principles governing adaptive behavior. While these frameworks are valued for their simplicity and interpretability, their reliance on few parameters often limits their ability to capture realistic biological behavior, leading to cycles of handcrafted adjustments that are prone to research subjectivity. Here, we present a novel modeling approach leveraging recurrent neural networks to discover the cognitive algorithms governing biological decision-making. We show that neural networks with just 1-4 units often outperform classical cognitive models and match larger neural networks in predicting the choices of individual animals and humans across six well-studied reward learning tasks. Critically, we then interpret the trained networks using dynamical systems concepts, enabling a unified comparison of cognitive models and revealing detailed mechanisms underlying choice behavior. Our approach also estimates the dimensionality of behavior and offers insights into algorithms implemented by AI agents trained in a meta-reinforcement learning setting. Overall, we present a systematic approach for discovering interpretable cognitive strategies in decision-making, offering insights into neural mechanisms and a foundation for studying both healthy and dysfunctional cognition.