Discovering cognitive strategies with tiny recurrent neural networks.

ABSTRACT: Understanding how animals and humans learn from experience to make adaptive decisions is a fundamental goal of neuroscience and psychology. Normative modelling frameworks such as Bayesian inference¹ and reinforcement learning² provide valuable insights into the principles governing adaptive behaviour. However, the simplicity of these frameworks often limits their ability to capture realistic biological behaviour, leading to cycles of handcrafted adjustments that are prone to researcher subjectivity. Here we present a novel modelling approach that leverages recurrent neural networks to discover the cognitive algorithms governing biological decision-making. We show that neural networks with just one to four units often outperform classical cognitive models and match lar

SUBMITTER: Ji-An L 

PROVIDER: S-EPMC12390849 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature